Advances in Real-Time Rendering in Games Part I
Bibliographic record
Abstract
Modern video games employ a variety of sophisticated algorithms to produce groundbreaking 3D rendering pushing the visual boundaries and interactive experience of rich environments.This course brings state-of-the-art and production-proven rendering techniques for fast, interactive rendering of complex and engaging virtual worlds of video games.In 2025, SIGGRAPH will celebrate the 20th anniversary of the Advances in Real-Time Rendering in Games program -one of the most enduring and influential research innovation forums in computer graphics.Since its inception in 2006, the program has served as a launchpad for groundbreaking rendering techniques that have fundamentally reshaped how artists, rendering engineers and game developers simulate lighting, geometry, and motion in real-time applications, especially in video games.From the introduction of physically based shading models and temporal antialiasing to breakthroughs in ray tracing and neuralenhanced image quality, the Advances in Real-Time Rendering in Games course has consistently spotlighted state-of-the-art techniques shaping the evolution of video games, virtual productions, architectural visualization, and interactive experiences at scale.The 2025 program will feature speakers from leading studios and engine teams including Activision, Ubisoft, Epic Games, id Software, MachineGames, HypeHype, and NVIDIA.As the 20th anniversary of the course, this year will also include a special retrospective honoring two decades of innovation, impact and shared technical progress in the real-time graphics community.The presenters will cover a wide range of topics, from innovations in subsurface scattering and real-time path tracing, new methods for performant order-independent transparency, practical
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".