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This paper addresses the challenge of precisely swapping objects in videos, particularly those involved in hand-object interactions (HOI), using a single user-provided reference object image. While diffusion models have advanced video editing, they struggle with the complexities of HOI, often failing to generate realistic edits when object swaps involve changes in shape or functionality. To overcome this, the authors propose HOI-Swap, a novel diffusion-based video editing framework trained in a self-supervised manner. The framework operates in two stages: (1) single-frame object swapping with HOI awareness, where the model learns to adjust interaction patterns (e.g., hand grasp) based on object property changes; and (2) sequence-wide extension, where motion alignment is achieved by warping a sequence from the edited frame using sampled motion points and conditioning generation on the warped sequence. Extensive qualitative and quantitative evaluations demonstrate that HOI-Swap significantly outperforms prior methods, producing high-quality, realistic HOI video edits. 

Generating realistic audio for human actions is critical for applications such as film sound effects and virtual reality games. Existing methods assume complete correspondence between video and audio during training, but in real-world settings, many sounds occur off-screen or weakly correspond to visuals, leading to uncontrolled ambient sounds or hallucinations at test time. This paper introduces AV-LDM, a novel ambient-aware audio generation model that disentangles foreground action sounds from ambient background noise in in-the-wild training videos. The approach leverages a retrieval-augmented generation framework to synthesize audio that aligns both semantically and temporally with the visual input. Trained and evaluated on Ego4D and EPIC-KITCHENS datasets, along with the newly introduced Ego4D-Sounds dataset (1.2M curated clips with action-audio correspondence), the model outperforms prior methods, enables controllable ambient sound generation, and shows promise for generalization to synthetic video game clips. This work is the first to emphasize faithful video-to-audio generation focused on observed visual content despite noisy, uncurated training data.