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VideoComposer: Compositional Video Synthesis with Motion Controllability

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arxiv 2306.02018 v2 pith:C36KNOK3 submitted 2023-06-03 cs.CV

classification cs.CV
keywords temporalvideoconditionsvideocomposerspatialsynthesiscompositionalconsistency
verification ladder T0 review T1 audit T2 compute T3 formal
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The pursuit of controllability as a higher standard of visual content creation has yielded remarkable progress in customizable image synthesis. However, achieving controllable video synthesis remains challenging due to the large variation of temporal dynamics and the requirement of cross-frame temporal consistency. Based on the paradigm of compositional generation, this work presents VideoComposer that allows users to flexibly compose a video with textual conditions, spatial conditions, and more importantly temporal conditions. Specifically, considering the characteristic of video data, we introduce the motion vector from compressed videos as an explicit control signal to provide guidance regarding temporal dynamics. In addition, we develop a Spatio-Temporal Condition encoder (STC-encoder) that serves as a unified interface to effectively incorporate the spatial and temporal relations of sequential inputs, with which the model could make better use of temporal conditions and hence achieve higher inter-frame consistency. Extensive experimental results suggest that VideoComposer is able to control the spatial and temporal patterns simultaneously within a synthesized video in various forms, such as text description, sketch sequence, reference video, or even simply hand-crafted motions. The code and models will be publicly available at https://videocomposer.github.io.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation

    cs.CV 2025-02 conditional novelty 7.0 of 10

    Users control a text-to-video model by arranging 3D bounding boxes and camera motion, and the system renders depth maps that steer the diffusion model to generate matching object and camera motion.

  2. TextMesh4D: Zero-shot Text-to-4D Mesh Generation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    TextMesh4D generates text-conditioned dynamic meshes by combining a Jacobian Deformation Field, video score distillation, and a local-global semantic regularizer in a zero-shot pipeline.

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