Over++: Generative Video Compositing for Layer Interaction Effects
In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation.
694b1f3d746a34b55dd53bd0
Luchao Qi
claimed_verified
2026-01-15T15:07:41.307Z
635734a5fe4ffe942db442b2
/avatars/34780e375254e109bff5ee9d3f43f5bd.svg
Luchao Qi
luchaoqi
user
luchaoqi
694b1f3d746a34b55dd53bd1
Jiaye Wu
694b1f3d746a34b55dd53bd2
Jun Myeong Choi
694b1f3d746a34b55dd53bd3
Cary Phillips
694b1f3d746a34b55dd53bd4
Roni Sengupta
694b1f3d746a34b55dd53bd5
Dan B Goldman
Over++: Generative Video Compositing for Layer Interaction Effects
In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation.
Over++, a video effect generation framework, synthesizes realistic environmental effects conditioned on text prompts while preserving the original video scene without requiring camera pose or dense annotations.
augmented compositing video generative models video inpainting text prompts input video layers Over++ environmental effects semi-transparent effects unpaired augmentation mask control keyframe guidance effect generation scene preservation