arXiv AI
Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
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ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits
arXiv:2609.00505v1 Announce Type: new Abstract: Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues...
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ELMA: Benchmarking Anaphoric Compositions for Long-Term Text-to-Motion Generation
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ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding
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TKCAM: Text and Keyframe to Camera Trajectory Generation
TKCAM is a new framework for generating controllable camera motion conditioned on text and sparse keyframes, using a 12‑dimensional kinematic representation and a two‑stage masked transformer with residual vector quantization. It allows users to provide free‑form text prompts and RGB observations at selected timestamps, enabling temporally localized visual guidance for coherent trajectory synthesis. The authors also introduce the RealEstate10K‑Cap dataset and a Universal CLaTr Evaluator, showing that TKCAM outperforms recent baselines on FID, text‑motion matching, and retrieval metrics.
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TKCAM: Text and Keyframe to Camera Trajectory Generation
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