Generative World Renderer
Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-buffer channels across diverse scenes, visual effects, and environments, including adverse weather and motion-blur variants. This dataset uniquely advances bidirectional rendering: enabling robust in-the-wild geometry and material decomposition, and facilitating high-fidelity G-buffer-guided video generation. Furthermore, to evaluate the real-world performance of inverse rendering without ground truth, we propose a novel VLM-based assessment protocol measuring semantic, spatial, and temporal consistency. Experiments demonstrate that inverse renderers fine-tuned on our data achieve superior cross-dataset generalization and controllable generation, while our VLM evaluation strongly correlates with human judgment. Combined with our toolkit, our forward renderer enables users to edit styles of AAA games from G-buffers using text prompts.
69cf1f95e02fdd68b91b7a47
Zheng-Hui Huang
69cf1f95e02fdd68b91b7a48
Zhixiang Wang
claimed_verified
2026-04-03T09:07:45.712Z
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/avatars/85b261bcdda4717a6e40491f6c7b7a89.svg
Zhixiang Wang
wangzx1994
user
wangzx1994
69cf1f95e02fdd68b91b7a49
Jiaming Tan
69cf1f95e02fdd68b91b7a4a
Ruihan Yu
69cf1f95e02fdd68b91b7a4b
Yidan Zhang
69cf1f95e02fdd68b91b7a4c
Bo Zheng
69cf1f95e02fdd68b91b7a4d
Yu-Lun Liu
69cf1f95e02fdd68b91b7a4e
Yung-Yu Chuang
69cf1f95e02fdd68b91b7a4f
Kaipeng Zhang
claimed_verified
2026-04-07T08:45:40.958Z
65f1713552c38a91e0a445e8
/avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
kaipeng
kpzhang996
user
kpzhang996
Generative World Renderer
Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-buffer channels across diverse scenes, visual effects, and environments, including adverse weather and motion-blur variants. This dataset uniquely advances bidirectional rendering: enabling robust in-the-wild geometry and material decomposition, and facilitating high-fidelity G-buffer-guided video generation. Furthermore, to evaluate the real-world performance of inverse rendering without ground truth, we propose a novel VLM-based assessment protocol measuring semantic, spatial, and temporal consistency. Experiments demonstrate that inverse renderers fine-tuned on our data achieve superior cross-dataset generalization and controllable generation, while our VLM evaluation strongly correlates with human judgment. Combined with our toolkit, our forward renderer enables users to edit styles of AAA games from G-buffers using text prompts.
A large-scale dynamic dataset derived from AAA games is introduced to improve generative inverse and forward rendering, featuring high-resolution synchronized RGB and G-buffer data alongside a novel VLM-based evaluation method that correlates well with human judgment.
G-buffer inverse rendering forward rendering VLM-based assessment cross-dataset generalization semantic consistency spatial consistency temporal consistency