AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Dif

arXiv:2609.20051v1 Announce Type: new Abstract: Step distillation reduces the cost of video generation, but reusing a LoRA trained for a longer trajectory can alter its functional

arXiv:2605.20758v1 Announce Type: new Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation

arXiv:2510.20579v2 Announce Type: replace-cross Abstract: Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence

arXiv:2603.18501v1 Announce Type: cross Abstract: Video compression aims to maximize reconstruction quality with minimal bitrates. Beyond standard distortion metrics, perceptual

arXiv:2605.15533v1 Announce Type: cross Abstract: Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data

arXiv:2606.17257v1 Announce Type: cross Abstract: Open-weight video diffusion models can generate photorealistic unsafe content, from violence to misinformation, yet existing

arXiv:2605.22158v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) face significant computational overhead when processing long videos due to the massive

arXiv:2608.11562v1 Announce Type: cross Abstract: Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks.