49 papers
This paper proposes DeformTrace, a hybrid architecture combining State Space Models with deformable dynamics and relay tokens to achieve state-of-the-art temporal forgery localization by addressing challenges in boundary ambiguity, sparse forgeries, and long-range modeling.
ExposureEngine is an end-to-end system that addresses the limitations of traditional horizontal bounding box methods by utilizing oriented bounding boxes and a language-driven agentic layer to provide accurate, rotation-aware sponsor visibility analytics and automated reporting for sports broadcasts.
EasyAnimate is a high-performance video generation framework that leverages diffusion transformers enhanced by Hybrid Window Attention for improved efficiency, reward backpropagation for better quality alignment, and additional optimizations like token-length training and multimodal text encoding to achieve state-of-the-art results.
The paper presents InstructHumans, a novel framework that introduces a modified Score Distillation Sampling for Editing (SDS-E) combined with spatial regularization and gradient-based sampling to enable instruction-driven 3D human texture editing that maintains high fidelity and consistency with the original avatar.
This paper demonstrates that diffusion-based image editing inherently compromises robust invisible watermarks by treating embedded payloads as noise to be removed during the denoising process, leading to a theoretical and empirical analysis of this fragility and proposing guidelines for future watermarking designs.
Crab is a scalable and unified audio-visual scene understanding model that overcomes the negative transfer issues of conventional multi-task methods by introducing the AV-UIE v2 dataset with explicit reasoning and an Interaction-aware LoRA mechanism to enable effective explicit cooperation across heterogeneous tasks.
This paper identifies and quantifies "Order-to-Space Bias" (OTS), a systematic flaw in modern image generation models where the textual order of entities incorrectly dictates their spatial layout, and demonstrates that this data-driven issue can be effectively mitigated through targeted fine-tuning and early-stage interventions without compromising generation quality.
This paper addresses the evaluation gap in music generation by introducing CMI-RewardBench, a comprehensive ecosystem comprising large-scale datasets, a unified benchmark, and efficient reward models to evaluate and improve music generation under complex compositional multimodal instructions.
This paper presents Export3D, a one-shot 3D-aware portrait animation method that utilizes a contrastive pre-training framework and a tri-plane generator to achieve expression-controllable, view-varying image synthesis while effectively eliminating undesirable appearance swaps during cross-identity expression transfer.