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MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation

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arxiv 2401.04468 v1 pith:HFNEBXWB submitted 2024-01-09 cs.CV cs.AI

Weimin Wang , Jiawei Liu , Zhijie Lin , Jiangqiao Yan , Shuo Chen , Chetwin Low , Tuyen Hoang , Jie Wu
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Jun Hao Liew Hanshu Yan Daquan Zhou Jiashi Feng
This is my paper · ORCID
classification cs.CVcs.AI
keywords videogenerationmagicvideo-v2modelmoduleaestheticallyarchitecturebenefiting
verification ladder T0 review T1 audit T2 compute T3 formal
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The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field. In this work, we introduce MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline. Benefiting from these architecture designs, MagicVideo-V2 can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness. It demonstrates superior performance over leading Text-to-Video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion model via user evaluation at large scale.

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Forward citations

Cited by 7 Pith papers

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

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    Phantom-Data provides around one million cross-context, identity-consistent reference-video pairs for subject-to-video generation, and training on it improves prompt following and visual quality.

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    StableAnimator++ combines learnable SVD-guided pose alignment, a distribution-aware ID Adapter, and an HJB-based inference-time face optimizer to preserve identity in human image animation under severe pose misalignment.

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