We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data.

Image diffusion distillation achieves high-fidelity generation with very few sampling steps.

TGNN[24]introduce3D-RESby extending the bounding box annotations of ScanRefer [5] to masks by incorporating the instance masks from ScanNet and proposed a two-stage pipeline. Further, 3D-STMN [65] proposed an end-to-end method that matches the text and superpoints to get the 3D segmentation of the target object directly.

In this paper, we are particularly interested in understanding whether, and how, the choice of distributional robustness bears statistical implications in learning the desired policy, by studying the sample complexity in the widely-used generative model (Kearns and Singh, 1999).

A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers.