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description=SV4D generates novel multi-view video synthesis from a single reference video.;
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sv4d dynamic 3d content generation with multi frame and multi view consistency sv4d dynamic 3d content generation with multi frame and multi view consistency yiming xie 1 2 chun han yao 1 vikram voleti 1 huaizu jiang 2 varun jampani 1 1 stability ai 2 northeastern university equal contribution equal advising arxiv model code blog sv4d takes a reference video as input and generates novel view videos and 4d models abstract we present stable video 4d sv4d a latent video diffusion model for multi frame and multi view consistent dynamic 3d content generation unlike previous methods that rely on separately trained generative models for video generation and novel view synthesis we design a unified diffusion model to generate novel view videos of dynamic 3d objects specifically given a monocular reference video sv4d generates novel views for each video frame that are temporally consistent we then use the generated novel view videos to optimize an implicit 4d representation dynamic nerf efficiently without the need for cumbersome sds based optimization used in most prior works to train our unified novel view video generation model we curated a dynamic 3d object dataset from the existing objaverse dataset extensive experimental results on multiple datasets and user studies demonstrate sv4d s state of the art performance on novel view video synthesis as well as 4d generation compared to prior works summary video results and comparison novel view video synthesis comparing our results with baselines 4d optimization comparing our results with baselines more results generated by sv4d bibtex article xie2024sv4d title sv4d dynamic 3d content generation with multi frame and multi view consistency author yiming xie and chun han yao and vikram voleti and huaizu jiang and varun jampani journal arxiv preprint arxiv 2407 17470 year 2024 this website is based on the template from nerfies