Goku provides a 2M-pair dataset for multi-task structural video editing, Goku-Edit model with MLLM and dual-branch design, and Goku-Bench yielding up to 8% gains in instruction following.
Omniv2v: Versatile video generation and editing via dynamic content manipulation
6 Pith papers cite this work. Polarity classification is still indexing.
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SpongeBob introduces the first end-to-end audio-visual joint editing framework using sync-aware bidirectional attention and context-aware modules, plus a new dataset and benchmark, claiming 30% Sync-C and 12.5% Ctx-F1 gains over baselines.
LIVEditor-14B applies a new sparse attention method (ISA) that prunes context and uses query-sharpness routing to cut attention latency ~60% with no loss in editing quality on standard benchmarks.
InsEdit adapts a video diffusion backbone for text-instruction video editing via Mutual Context Attention, achieving SOTA open-source results with O(100K) data while also supporting image editing.
A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.
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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Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing
Goku provides a 2M-pair dataset for multi-task structural video editing, Goku-Edit model with MLLM and dual-branch design, and Goku-Bench yielding up to 8% gains in instruction following.
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SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing
SpongeBob introduces the first end-to-end audio-visual joint editing framework using sync-aware bidirectional attention and context-aware modules, plus a new dataset and benchmark, claiming 30% Sync-C and 12.5% Ctx-F1 gains over baselines.
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LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention
LIVEditor-14B applies a new sparse attention method (ISA) that prunes context and uses query-sharpness routing to cut attention latency ~60% with no loss in editing quality on standard benchmarks.
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InsEdit: Towards Instruction-based Visual Editing via Data-Efficient Video Diffusion Models Adaptation
InsEdit adapts a video diffusion backbone for text-instruction video editing via Mutual Context Attention, achieving SOTA open-source results with O(100K) data while also supporting image editing.
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Image-to-Video Diffusion: From Foundations to Open Frontiers
A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.
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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.