News

Steven Anson Coons

 Leo Guibas                guibas@cs.stanford.edu

ACM SIGGRAPH is pleased to present Leonidas J. Guibas with the 2025 Steven Anson Coons Award for Outstanding Creative Contributions to Computer Graphics. He is being recognized for his research contributions to geometry processing and rendering, geometric deep learning, and global illumination — honoring an individual who has made a lifetime contribution to computer graphics and interactive techniques.

Guibas began his research career in theoretical computer science, completing a PhD advised by Donald Knuth at Stanford University in 1976. He initially published important algorithms and data structures for hashing, sorting and searching. These include concepts taught in computer science programs worldwide such as red-black trees for self-balancing binary trees and algorithms for pattern matching in strings.

After his PhD, he worked in industry at the Xerox Palo Alto Research Center (PARC) and then the Systems Research Center, Digital Equipment Corporation, while also joining Stanford University as a Professor of Computer Science. In 1982, Guibas published a paper in the third ever issue of ACM Transactions on Graphics, which started with the line “This paper proposes that bitmaps, or raster images, should be given full citizenship in the world of computer science.” Around this time, Guibas’ attention turned to computational geometry, where his key early results would define the field’s success for decades. Among these were a groundbreaking  divide-and-conquer algorithm for Voronoi diagram construction via its dual Delaunay triangulation, in a paper that also introduced the quad-edge data structure widely used in geometric modeling. In fact, Guibas led an early movement within computational geometry to leverage the power of geometric duality. He also introduced to computational geometry a kinetic framework that, for example, allows determining the swept region of one shape along a curve: a core subroutine of font design and digital drawing. His concept of kinetic data structures, which track attributes of interest in an evolving system over (perhaps virtual) time, has routinely appeared as an ingredient of SIGGRAPH papers for applications in physically based simulation (contact / collision handling) or rendering (visibility queries).

Guibas’s early work on visibility problems within 2D polygons laid the foundation for computer graphics rendering systems over the next decades. His work on the computational complexity for determining all intersections of a set of curves laid the foundation for powerful geometric algorithm tools such as the CGAL library, a standard tool in geometry processing known for robustness. With his PhD student David Salesin (2000 Computer Graphics Achievement Award), Guibas demonstrated how inexact calculations, using floating point math, could still lead to robust and provably correct geometric constructions. 

Together with his PhD student Eric Veach, Guibas pioneered realistic rendering algorithms that remain central to today’s state of the art. They introduced bidirectional path tracing, which enables tracing rays from both the light source and the eye, addressing the long-standing problem that different optical effects are easier to sample from one or the other. They then introduced multiple importance sampling, which dramatically reduces variance when combining multiple estimators, as is the case with sampling according to a BRDF vs. light source. Finally, they developed Metropolis light transport, which remains the most powerful unbiased approach to the simulation of arbitrary light paths including complex specular effects and caustics — a method extensively in the special effects industry. Veach received a 2014 Academy Scientific and Engineering Award for his PhD work.

In the late 1990s, Guibas made new insights to earth mover’s distance: the problem of moving masses from one distribution to another while minimizing the total distance traveled. This unlocked key metrics for image processing to measure the distance between images or distributions of images, and eventually to the broader community of machine learning as metrics or loss functions for distribution matching. With his PhD student Justin Solomon (2022 Significant New Researcher Award), Guibas demonstrated the power of earth mover’s distance (also referred to as Wasserstein distance or optimal transport) for complex matching problems involving discrete surfaces.

As geometry processing crystallized into its own subcommunity, Guibas repeatedly made key contributions including the heat kernel signature, functional maps and map networks, surface segmentation, and symmetry detection. His functional maps work received the Siggraph 2023 Test-of-Time paper award. Guibas was also at the forefront of machine learning for 3D shapes, making early contributions to convolutional neural networks on 3D shapes and shape retrieval networks. His work on PointNet and PointNet++ was a breakthrough in geometric learning, allowing for the first time to directly train networks over point clouds as input. He initiated and co-developed the ShapeNet dataset, which was the first large scale annotated dataset of 3D models, and stood as the gold standard in the community for over a decade.

Many of Guibas’s trainees are now important faculty and industry leaders in the fields of computer graphics, vision and learning. He has continued to cultivate industrial connections and applications of his work and is currently a principal scientist at Google Deep Mind, in addition to being a Professor at Stanford University. He has been elected a member of the National Academy of Engineering, the National Academy of Sciences, the American Academy of Arts and Sciences, and also as an ACM and IEEE Fellow.

Computer Graphics Achievement

George Drettakis         George.Drettakis@inria.fr

ACM SIGGRAPH is pleased to present the 2025 Achievement Award to George Drettakis for pioneering work in shadow computation, sound rendering, and image-based rendering, including 3D Gaussian splatting.

George’s work on rendering has been highly influential, characterized by methods that are both principled and practical. This includes his foundational work on discontinuity meshing and visibility skeletons for accurate shadow computation, the development of perspective shadow maps to significantly reduce aliasing in shadow maps, and progress in hierarchical global illumination methods. His contributions on texture and materials include the use of Gabor noise  as well as an early learning-based material estimation method.  Perceptually-informed rendering has also been a key theme of George’s work, where his notable contributions have advanced the state of the art in both sound rendering and virtual reality.

George has also been at the forefront of the developments in image-based rendering, developed jointly with his students, postdocs, and collaborators.  His work on depth synthesis and local warps demonstrated how to create high fidelity interpolations of captured images.  His work on deep blending IBR was among the first to apply neural networks to improve the results of classical view-based interpolation.  His most recent contribution in this area has been the phenomenal breakthrough of 3D Gaussian splatting for radiance field rendering, combining ideas from earlier point-based rendering with ideas in volumetric rendering used in neural radiance fields. It has not only generated hundreds of follow-on publications in the two years since it appeared but has also become a de-facto standard for efficient 3D reconstruction and real-time rendering of highly realistic (and even dynamic) scenes.

George received a Bachelor of Science degree from the University of Crete and M.Sc. and Ph.D. degrees in Computer Science from the University of Toronto in 1994. He was an ERCIM postdoctoral fellow in Grenoble, Barcelona and Bonn.  Since 1995, he has been a researcher with INRIA, first in Grenoble,  where he was awarded his “Habilitation” at the University of Grenoble (1999). In 2000, he moved to INRIA Sophia-Antipolis, where he now leads the GRAPHDECO research group.

For over 30 years, he has taken on key leadership roles in the computer graphics community, including technical papers chair of SIGGRAPH Asia 2010, Associate Editor for ACM TOG, and chairing the ACM SIGGRAPH Papers Advisory Group. He has had a key role in shaping computer graphics in Europe, as chair of the EUROGRAPHICS Steering Committee for the Working Group on Rendering, co-chairing the Eurographics Workshop on Rendering (1998), and as papers co-chair of the Eurographics Conference (2002 and 2008). George has mentored more than twenty Ph.D. students and as many post doctoral fellows who have gone on to have significant impact of their own in industry and academia. 

Significant New Researcher

Ben Mildenhal             me@bmild.com      

Pratul Srinivasan         pratul.srinivasan@gmail.com

ACM SIGGRAPH is pleased to present the 2024 Significant New Researcher Award to Ben Mildenhall and Pratul Srinivasan for their outstanding contributions to new representations for 3D graphics, neural rendering, novel view synthesis, and generative models of 3D scenes.

Ben and Pratul’s research centers on new representations and algorithms for capturing, rendering, and generating 3D scenes. Before their work, computer graphics had largely settled on explicit surface-based representations, such as triangles and subdivision surfaces together with textures and BRDF for material appearance. However, these representations made it challenging to capture fully realistic models of real scenes and to synthesize scenes. They were also poorly suited to new deep learning methodology because their hardness and arbitrary topology did not interact well with optimization techniques and derivative computations.

Ben and Pratul introduced radically new representations and algorithms for the neural age that have dramatically improved our ability to capture the real world and synthesize 3D scenes. Their work on the Neural Radiance Field (NeRF) introduced a fundamentally new way of representing 3D scenes, combining volume rendering and neural networks as the scene representation itself. They also analyzed and demonstrated the importance of Fourier features to achieve high-resolution representations. They then introduced multiple new representations to scale up neural representations to richer scenes and higher-fidelity reconstructions, including Mip-NeRF, Mip-NeRF360, Block-NeRF, and Zip-NeRF.

They also made important contributions to computational imaging using advanced priors, in the areas of black hole imaging for Pratul and lensless diffuser cameras for Ben.

Ben and Pratul also spearheaded the area of generative AI for 3D. In DreamFusion, Ben and co-authors were the first to demonstrate the use of 2D diffusion models to train the synthesis of 3D models from text prompts, sidestepping the need for 3D training data. In CAT3D, Pratul and co-authors introduced a multi-view latent diffusion model to generate novel views of a scene.

In summary, Ben Mildenhall and Pratul Srinivasan revolutionized the full gamut of 3D graphics from the capture of real scenes with neural representations, to computational imaging, all the way to generative models for the synthesis of 3D scenes.

Ben Mildenhall received a B.S from Stanford University in 2015 and a PhD from UC Berkeley in 2020 advised by Ren Ng. During his PhD, he interned with Jon Barron at Google Research and with Rodrigo Ortiz-Cayon and Abhishek Kar at Fyusion. He was a research scientist at Google Research before cofounding World Labs.

Pratul Srinivasan received a B.S.E. from Duke University in 2014 and a PhD from UC Berkeley in 2020, where he was supervised by Ren Ng and Ravi Ramamoorthi. During his PhD, he interned at Google Research with Jon Barron and Noah Snavely. He is currently a research scientist at Google DeepMind.

Outstanding Doctoral Dissertation

Rohan Sawhney         rsawhney1993@gmail.com     

ACM SIGGRAPH is pleased to announce Rohan Sawhney as the 2025 recipient of the Outstanding Doctoral Dissertation Award. The dissertation coherently integrates deep theory, algorithm development, and practical implementation, enabling efficient simulations on highly detailed, complex geometries. Sawhney’s thesis proposes and explores a fundamental paradigm-shift in the way geometry processing and physical simulations are performed in Computer Graphics and other fields. By using Monte Carlo techniques he avoids the critical and error-prone domain discretization step that is required by traditional finite element methods. The immense potential and deep insight of Rohan’s work emerges from a unifying Monte Carlo perspective on geometry processing, for simulation and rendering.

Sawhney starts out from the observation that geometric models in Computer Graphics today are complex and intricate, have rich surface detail, and depending on the source, may have self-intersections, holes or other artifacts that prevent them from being easily used in geometry processing and simulation frameworks for partial differential equations. Moreover, one might seek solutions on only a sub-region of the geometry, rather than requiring to simulate the entire region that may have millions or billions of polygons. Thus, he asks a revolutionary question of whether we can develop grid-free methods that simply operate in the continuous volume for simulation without requiring a discrete grid or mesh. The audacity of this question is noteworthy: for decades physical simulation in Computer Graphics has relied on the finite element method utilizing discretized domains. While Computer Graphics has previously exploited Monte Carlo methods in rendering, there was never a clear analogy in physical simulation approaches, which largely remained finite element- based. Upending this paradigm in simulation is a truly revolutionary advance.

In order to accomplish this goal, a broad range of scientific innovations is required, covering the entire spectrum from sophisticated mathematical formulations to highly efficient parallel algorithms and powerful geometric data structures. Rohan Sawhney’s dissertation has already inspired a considerable amount of follow-up work by other researchers in the field and has proven superior in many practical applications.

Outstanding Doctoral Dissertation Honorable Mention

Suyeon Choi   suyeon@stanford.edu

For a dissertation on ground-breaking work towards the development of holographic near eye displays

Outstanding Doctoral Dissertation Honorable Mention

Silvia Sellan    silviasellan@cs.columbia.edu

For a dissertation on robustly and reliably processing geometric models “in the wild“.

Outstanding Service

Mk Haley        mkhaley@utexas.edu.

For her over thirty-five years of outstanding and impactful service to the computer graphics community, ACM SIGGRAPH recognizes Mk Haley with the 2025 Outstanding Service Award.

Mk, who has been a SIGGRAPH member since 1989, has contributed to the ACM SIGGRAPH organization, the SIGGRAPH Conference, and ACM.

Her contributions to the organization began with the SIGGRAPH Education Committee where she served from 1990-1997. She was the SPACE Art Show Chair and with Jackie White, was co-editor of the Education Committee Newsletter. In 2004-2007 she was the ACM SIGGRAPH Director of Communications. In 2017-2020 she was the SIGGRAPH Conference Frontiers Program Chair while the Program was getting started as an organizational effort. It became a regular SIGGRAPH Conference venue in 2021. She has also worked with the S3 Committee (SIGGRAPH Student Services, currently renamed as the Early Career Development Committee) as a program advisor and a participant mentor.

In 2010-2012, she served on the ACM-Computers in Entertainment Board of Editors

She has served in different aspects of the SIGGRAPH Conference beginning in 1989 when she hosted 11 Student Volunteers at her parent’s house for the Boston SIGGRAPH Conference. She served as a SIGGRAPH Conference Student Volunteer in 1990-1992 and as a SIGGRAPH Conference Student Volunteer Team Leader for 1993-1995. In 1997, she was the SIGGRAPH Conference Student Volunteer Chair. In 2001, she was the SIGGRAPH Conference Emerging Technologies Chair. She was the SIGGRAPH Conference Chair for 2013 and served on the SIGGRAPH Conference Advisory Group from 2011-2014. In 2022, she was the SIGGRAPH Conference Emerging Technologies Chair. She has also been a frequent Conference content juror and onsite panel moderator/papers session wrangler. She is the SIGGRAPH 2026 RTL! (Real Time Live!) Venue Chair.

Her most substantial contributions were to the Conference Student Volunteer and Emerging Technologies Programs. For the Student Volunteer Program, she helped guide it through several evolutions as it expanded and contracted over time, and she mentored students toward both professional success and more senior roles within the ACM SIGGRAPH organization. For the Emerging Technologies Program she helped it to evolve and remain a consistent and distinctive part of the Conference.

In her service activities, Mk has exhibited strong qualities of leadership and expertise. With this award, ACM SIGGRAPH celebrates Mk Haley’s many contributions to both the SIGGRAPH Conference and to the organization.        

Distinguished Artist

Frieder Nake               nake@informatik.uni-bremen.de nake@uni-bremen.de       

The 2025 ACM SIGGRAPH Distinguished Artists Award for Lifetime Achievement in Digital Art is given to Frieder Nake in recognition of his foundational work in algorithmic art and his lasting contributions to computer graphics and digital art. Active since the early 1960s, Nake’s work helped define the field at its inception and continues to influence thinking around aesthetics and computation.

Frieder Nake is one of the founders of digital, or more accurately, algorithmic art, which was known as “computer art” when it emerged in the early 1960s. He produced his first works in 1963 and had his first exhibition of drawings in November 1965 at the Galerie Wendelin Niedlich, Stuttgart, Germany.

Nake’s work was included in what is considered the benchmark computer art exhibition, one of the two defining exhibitions of 1968: “Cybernetic Serendipity” at the Institute of Contemporary Arts in London, curated by Jasia Reichardt. In the same year, his work was included in the exhibition “Tendencies 4” at the Gallery of Contemporary Art in Zagreb.

Max Bense’s Information Aesthetics influenced his early work, an approach to the field of traditional aesthetics based on the mathematical concept of “information,” as developed by Claude E. Shannon and others.

Nake’s (2004/2005) slightly ironic statements may characterize his position and approach, “The drawings were not very exciting. But the »principle« was! ” and “Think the image, don’t make it.”

From 1963 to 1969, he pursued a succession of increasingly complex programs, utilizing technical support that evolved from machine language to Fortran IV, Algol 60, and finally PL/I. His main work phases are identified by collections of programs called “compArt ER56” (1963-65), Walk-through-raster (1966), Matrix multiplication (1967/68), and Generative Aesthetics I (1968/69).

He decided not to continue producing computer art in 1971 when he published a note under the title “There should be no computer art” on Page in the Bulletin of the Computer Arts Society. His reasons were mainly of political origin: He did not see how he could actively contribute to computer art and, at the same time, be a political activist against capitalism. He resumed publishing on computer art in the mid-1980s with the break-down of the radical left in Western Europe.

In 1999, with his project “compArt,” Nake returned to his roots as a theoretician, writer, creator, and teacher (and artist) in the domain of digital art and way beyond. He was the head of “compArt: Center of Excellence in Digital Art” until the end of this vibrant and successful group in 2016.

Frieder Nake has been a full professor of computer science at the University of Bremen, Germany, since 1972. From 2005 to 2019, he also taught at the University of the Arts, Bremen. His teaching and research activities focus on computer graphics, digital media, computer art, the design of interactive systems, computational semiotics, and the general theory of computing. He has published in all the areas mentioned above, with a preference for computer-generated images.

His artworks are in many collections, including the Tate Gallery,  London; Victoria and Albert Museum,  London; Sprengel Museum, Hanover, Germany; Musée National Des Beaux-Arts Du Québec, Canada; Museum of Contemporary Art, Zagreb, Croatia; Kunsthalle Bremen, Germany; and the Mary and Leigh Block Museum of Art, Evanston, Illinois.

Throughout his career, Nake has demonstrated an unwavering commitment to advancing the field through research, education, and collaboration. His influential publications have not only contributed to the academic discourse but have also served as a guiding light for emerging generations of artists and researchers exploring the potential of computational processes in art. As one of the early pioneers in the field, his groundbreaking research and artistic endeavors have inspired countless individuals and laid the foundation for the evolution of computer graphics as a legitimate form of creative expression. In addition to his scholarly achievements, Frieder Nake’s dedication to fostering a vibrant community around computer art and graphics has left an indelible mark. His mentorship and leadership have played a pivotal role in nurturing talent and fostering innovation within the SIGGRAPH community and beyond.

We are honored to add to Frieder Nake’s list of accolades the 2024 ACM SIGGRAPH Distinguished Artists Award for Lifetime Achievement in Digital Art for his foundational work in algorithmic art and his lasting contributions to computer graphics and digital art.

Distinguished Educator

Glenn Goldman           glenn.goldman@njit.edu     

ACM SIGGRAPH is pleased to present the 2025 Distinguished Educator Award to Glenn Goldman in recognition of the pioneering work and sustained innovation in introducing and integrating computer graphics and interactive techniques to architecture and design students and professionals for more than four decades.

Glenn Goldman, Director Emeritus and Professor of Architecture and Design at the New Jersey Institute of Technology (NJIT), has been active in computer graphics and interactive techniques for more than 40 years. A registered architect, he earned a BA degree from Columbia and the M. Arch. degree from Harvard. Following teaching positions at Iowa State University and the Boston Architectural Center, he joined the faculty at NJIT in 1982.

With a record of innovation, Glenn Goldman was one of the first architects to recognize the potential of computer graphics and three-dimensional modeling for architectural education, and with a colleague at NJIT created fully electronic design studios with color pre-visualization making use of customized personal computers and an eclectic collection of software applications. He took a central role in the transformation of architectural design studios from analog to digital workflows, and his work and that of his students were published in both mainstream design press magazines like Architectural Record and Interiors, as well as in academic and professional conferences like ACADIA (the Association for Computer-Aided Design in Architecture). After more than a decade of implementation and experimentation, he authored Architectural Graphics: Traditional and Digital Communication published by Prentice Hall, the first textbook for architectural communication incorporating computer graphics that served as a practical and transitional guide for educators and their students.

He authored or co-authored more than fifty articles that have appeared in journals, magazines, and/or presented at various conferences. From early use of animation and scripted walkthroughs and the integration of objective and non-objective sound in architectural proposals, to the use of building information modeling, image sampling, game engines, VR, and AI for architectural and interior design, he has consistently pushed the limits of technology in his classes and design studios exposing his students to processes not yet widely adopted, and preparing them for leadership positions in industry. His students have been winning design awards and getting their work published in magazines and books for almost as long as he’s been teaching.

In 2007, he started the School of Art + Design at NJIT and included in their offerings a generalized (and NASAD-accredited) Digital Design degree program that was based, in part, on the diverse subject matter typically found at a SIGGRAPH conference and one that emphasizes collaborative interdisciplinary work.

Glenn Goldman has been involved with the education community at SIGGRAPH for a long time. He was an early contributor to the annual curated Faculty Submitted Student Work exhibit which led to his involvement with the SIGGRAPH Education Committee, a group he has chaired since 2021. He was the Education Liaison for SIGGRAPH 2013 and Courses Chair for the 2015 conference. He developed the industry/academy panel that has become a staple of the Educators Forum and chaired the first four iterations of the event. And he’s appeared on a variety of panels both in person and online – including the “Pioneers of CGI Education” that was part of the SIGGRAPH Pioneers of Computer Graphics series in 2023 and “Computer Graphics and Architectural Design” at SIGGRAPH in 1991.

Goldman has been a staunch advocate for underserved and first-generation college students, ensuring that new technology is a tool for opportunity. A recipient of both internal and external awards for his teaching, his former students have gone on to work in industry at places like NASA, Gensler, AECOM, Ware Malcomb, M Moser, ENV, NBC Universal, NFL Films, Chaos, Spring AI, PowerHouse VFX, Framestore, ETS, Verizon, Autodesk, Walt Disney Co., Sony Pictures Imageworks, Ford Motor Company, and more. Others have gone on to teach at universities including Harvard, Pratt, Temple, Clemson, RISD, and NJIT.

An individual who has embraced his roles as a mentor and leader, Glenn Goldman has influenced industry and academic practices in architecture and design, and has made a career in providing opportunities to students and educators alike.

Practitioner

 Brian Karis                 brian.karis@epicgames.com       

ACM SIGGRAPH is pleased to recognize Brian Karis with the 2025 Practitioner Award. The Practitioner Award is given annually to practitioners who have had an impact on computer graphics and interactive techniques.

For more than a decade, Brian has been a driving force in advancing real-time rendering technology. As a key innovator, Brian revolutionized the handling of high-detail assets with the creation of Nanite in Unreal Engine, enabling the rendering of massive geometric complexity without traditional level-of-detail systems. Brian was also instrumental in refining Epic Games’s physically-based shading model, which has become a standard in Unreal Engine and influenced the broader game industry. Additionally, his advancements in temporal anti-aliasing have significantly improved image stability and quality in modern game engines.

Brian also actively shares his insights with the community through SIGGRAPH presentations and public talks, influencing rendering techniques in other engines and research in real-time graphics. His dedication to bridging the gap between research and practical solutions has made him a key figure in the field.

For his work on Physically-Based Rendering, Temporal Anti-Aliasing, and Nanite, ACM SIGGRAPH is proud to give the 2025 Practitioner Award to Brian Karis.

SIGGRAPH Academy

 Leif Kobbelt    kobbelt@cs.rwth-aachen.de

For pioneering contributions and leadership in the fields of geometry processing and multiscale modeling.

John (Spike) Hughes  jfh@cs.brown.edu

For outstanding contributions and sustained leadership to computer graphics education and research.

Richard (Hao) Zhang  haoz@sfu.ca

For technical contributions in spectral and learning-based methods for geometric modeling.

Karol Myszkowski       karol@mpi-inf.mpg.de

For foundational contributions to perceptual rendering and High Dynamic Range imaging in graphics.