LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis
Hanlin Wang, Hao Ouyang, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Qifeng Chen, Yujun Shen, Limin Wang
2025Year
18Top-tier citations
Abstract
Near Far Figure 1 . LeviTor is capable of generating videos with controlled occlusion, better depth changes, and complex 3D orbiting movement based on user inputs. Given an initial frame, users can easily draw 3D trajectory using our inference pipeline to represent their desired movements for designated area. We highly recommend viewing the supplementary materials for detailed video demonstrations.
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Install the CLIlune papers fulltext f289b544-7906-47a3-b86c-64a046981e46Cited by top-tier papers18
- Wan-Move: Motion-controllable Video Generation via Latent Trajectory GuidanceRuihang Chu, Yefei He, Zhekai Chen, Shiwei Zhang et al.NeurIPS 2025 · 50 citations
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Builds on33
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 6,759 citations
- Video Diffusion ModelsJonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan et al.NeurIPS 2022 · 2,948 citations
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