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MLLM-Guided Semantic Correction for Text-to-Video Generation
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arXiv AI
Aug 18
MLLM-Guided Semantic Correction for Text-to-Video Generation
arXiv:2608. 16513v1 Announce Type: cross Abstract: Recent advances in diffusion models and Transformer architectures have led to significant progress in text-to-video generation.
arXiv AI
Sep 3
Bernini: Latent Semantic Planning for Video Diffusion
Bernini proposes a unified framework that separates semantic planning and pixel rendering for video generation and editing. An MLLM-based planner predicts target semantics in ViT embedding space, while a DiT-based renderer synthesizes pixels conditioned on this plan, text features, and source VAE features for editing. The approach introduces Segment-Aware 3D Rotary Positional Embedding and chain-of-thought reasoning, achieving state‑of‑the‑art performance on diverse video benchmarks.
arXiv AI
5d ago
VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models
VIDiff is a unified foundation model that uses diffusion techniques to perform a broad range of video tasks, including both understanding tasks like language‑guided video object segmentation and generative tasks such as video editing and enhancement. Unlike prior methods that focus on short clips and require time‑consuming tuning, VIDiff can edit and translate videos within seconds based on user instructions and employs an iterative auto‑regressive approach to maintain consistency in long‑form videos. The authors demonstrate convincing generative results across diverse input videos and written instructions, supported by qualitative and quantitative evidence.
arXiv Computer Vision
Sep 15
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
arXiv:2512.07480v2 Announce Type: replace Abstract: While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates...
arXiv AI
Aug 13
Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
arXiv:2608. 12290v1 Announce Type: cross Abstract: Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows.
arXiv AI
Sep 30
PreviewDiff: Multimodal Critic-Guided Search over Diffusion Latents
PreviewDiff is a test‑time search method that uses multimodal critics to guide diffusion model sampling. By decoding partial previews at selected denoising checkpoints, scoring them with a multimodal judge, and branching over semantic prompt edits, it allows the generation process to be edited and rerouted before completion. The approach consistently outperforms budget‑matched Best‑of‑N sampling and scalar‑search baselines on image and video benchmarks, with early interventions and wider search yielding the biggest gains.