TeDiO regularizes temporal diagonals in diffusion transformer attention maps to produce smoother video motion while keeping per-frame quality intact.
Motionclone: Training-free motion cloning for controllable video generation
8 Pith papers cite this work. Polarity classification is still indexing.
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NeoMap introduces a training-free framework using convergent manifold alternating projection iterations to extract high-fidelity novel views from pre-trained video models, outperforming prior methods on standard benchmarks.
TriMotion is a modality-agnostic framework that maps video, pose, and text descriptions of the same camera trajectory into a shared motion embedding space, trained with a new triplet dataset and latent consistency objective, to produce videos that follow the target trajectory.
RealCam is a causal autoregressive model for real-time camera-controlled video-to-video generation, using cross-frame in-context teacher distillation and loop-closed data augmentation to achieve high fidelity and consistency.
INSPATIO-WORLD is a real-time framework for high-fidelity 4D scene generation and navigation from monocular videos via STAR architecture with implicit caching, explicit geometric constraints, and distribution-matching distillation.
Training-free motion conditioning for latent video diffusion by direct injection of low-frequency phase from a reference video into the diffusion noise.
EasyVFX decouples VFX generation via frequency-aware Mixture-of-Experts and test-time training to achieve realistic effects with limited resources.
This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.
citing papers explorer
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TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion
TeDiO regularizes temporal diagonals in diffusion transformer attention maps to produce smoother video motion while keeping per-frame quality intact.
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NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
NeoMap introduces a training-free framework using convergent manifold alternating projection iterations to extract high-fidelity novel views from pre-trained video models, outperforming prior methods on standard benchmarks.
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TriMotion: Modality-Agnostic Camera Control for Video Generation
TriMotion is a modality-agnostic framework that maps video, pose, and text descriptions of the same camera trajectory into a shared motion embedding space, trained with a new triplet dataset and latent consistency objective, to produce videos that follow the target trajectory.
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RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control
RealCam is a causal autoregressive model for real-time camera-controlled video-to-video generation, using cross-frame in-context teacher distillation and loop-closed data augmentation to achieve high fidelity and consistency.
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INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling
INSPATIO-WORLD is a real-time framework for high-fidelity 4D scene generation and navigation from monocular videos via STAR architecture with implicit caching, explicit geometric constraints, and distribution-matching distillation.
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{\Phi}-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation
Training-free motion conditioning for latent video diffusion by direct injection of low-frequency phase from a reference video into the diffusion noise.
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EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation
EasyVFX decouples VFX generation via frequency-aware Mixture-of-Experts and test-time training to achieve realistic effects with limited resources.
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Evolution of Video Generative Foundations
This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.