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SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models

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arxiv 2311.16933 v1 pith:LXY7C3CI submitted 2023-11-28 cs.CV

Yuwei Guo , Ceyuan Yang , Anyi Rao , Maneesh Agrawala , Dahua Lin , Bo Dai This is my paper · ORCID
classification cs.CV
keywords sparsectrldepthsignalssparsecontrolmodelsstructuretext
verification ladder T0 review T1 audit T2 compute T3 formal
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The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at https://guoyww.github.io/projects/SparseCtrl .

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Cited by 5 Pith papers

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

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    STARCaster is a 2D spatio-temporal video diffusion model that unifies identity-conditioned audio-driven portrait animation and novel-view synthesis without explicit 3D reconstruction.

  2. TokensGen: Harnessing Condensed Tokens for Long Video Generation

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    TokensGen generates consistent long videos by representing each clip as condensed semantic tokens, generating all tokens jointly from text, and stitching clips with adaptive FIFO denoising.

  3. EX-4D: EXtreme Viewpoint 4D Video Synthesis via Depth Watertight Mesh

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    EX-4D uses a depth watertight mesh and simulated occlusion masks to condition a video diffusion model for extreme-viewpoint 4D video synthesis from monocular input.

  4. Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A three-stage pipeline combining sparse 'tile' attention with multi-step consistency distillation makes Open-Sora-Plan video generation up to 7.8x faster while keeping the aggregate VBench final score within 1%.

  5. LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LiON-LoRA adds a learned scaling token to video-diffusion LoRA adapters, enabling linear and independent control of camera trajectory and object motion strength.

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