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TreeSRNF reveals a groundbreaking way to model and generate complex tree-like structures, outperforming traditional methods in both accuracy and versatility.
Video-LLMs can achieve more reliable reasoning by first constructing a compact, structured representation of salient events and their causal relationships.
Forget tedious pose annotations: this text-to-video approach generates realistic acrobatic human motions by cascading a text-to-skeleton model with a pose-conditioned diffusion model.
