Game Editing is a fine-tuned video diffusion model for controllable game video synthesis. It enables users to manipulate lighting and environmental effects in game footage via text prompts, conditioned on G-buffer inputs.
| Attribute | Detail |
|---|---|
| Base Model | Wan 2.1-T2V-1.3B |
| Parameters | 1.42B (BF16) |
| Resolution | 832 × 480 (480p) |
| Frame Rate | 16 FPS |
| Clip Length | 81 frames |
| Format | SafeTensors |
The model takes the following inputs:
The G-buffers encode the scene's geometry and materials, while the text prompt controls lighting conditions, atmospheric effects, and overall visual style. This decoupled design allows users to edit the visual appearance of game footage without altering the underlying scene structure.
We adapt Wan 2.1-T2V-1.3B by incorporating G-buffers (dense geometric and material priors) as conditional inputs. The model is fully fine-tuned following the original training configuration of the base model.
The model is trained on video clips from the Black Myth: Wukong dataset. Descriptive captions for each clip are generated using Qwen3-VL-235B-A22B-Instruct. Since G-buffers already provide dense geometric and material information, the captions focus exclusively on lighting and environmental effects, enabling fine-grained text-based control over these attributes during inference.
In the absence of directly comparable methods, we establish a baseline by adapting DiffusionRenderer's forward renderer with DiffusionLight-extracted environment maps as lighting conditions.
If you find this model useful, please consider citing our work.
1 commits
Game Editing is a fine-tuned video diffusion model for controllable game video synthesis. It enables users to manipulate lighting and environmental effects in game footage via text prompts, conditioned on G-buffer inputs.
| Attribute | Detail |
|---|---|
| Base Model | Wan 2.1-T2V-1.3B |
| Parameters | 1.42B (BF16) |
| Resolution | 832 × 480 (480p) |
| Frame Rate | 16 FPS |
| Clip Length | 81 frames |
| Format | SafeTensors |
The model takes the following inputs:
The G-buffers encode the scene's geometry and materials, while the text prompt controls lighting conditions, atmospheric effects, and overall visual style. This decoupled design allows users to edit the visual appearance of game footage without altering the underlying scene structure.
We adapt Wan 2.1-T2V-1.3B by incorporating G-buffers (dense geometric and material priors) as conditional inputs. The model is fully fine-tuned following the original training configuration of the base model.
The model is trained on video clips from the Black Myth: Wukong dataset. Descriptive captions for each clip are generated using Qwen3-VL-235B-A22B-Instruct. Since G-buffers already provide dense geometric and material information, the captions focus exclusively on lighting and environmental effects, enabling fine-grained text-based control over these attributes during inference.
In the absence of directly comparable methods, we establish a baseline by adapting DiffusionRenderer's forward renderer with DiffusionLight-extracted environment maps as lighting conditions.
If you find this model useful, please consider citing our work.
1 commits