AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction
Predicting robot videos requires both precise motion reasoning and preservation of high-frequency appearance, yet monolithic pixel models entangle these objectives and often conceal their progress behind a strong last-frame baseline. We present AcrossVAM1.0, a lightweight, text-assisted video action model that factorizes future prediction into object-centric motion and dense appearance. A frozen SAM3-DLP codec decomposes four context frames into semantic particles for the robot, arm, and gripper, together with a background latent. A 0.28M-parameter spatio-temporal Transformer aligns particle i
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- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “AcrossVAM1.0: Particle World Modeling for Text-Assisted Robo” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Yafei Zhang →
“AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction”
- LinkedLinked via arxiv author · 85%Nan Wu →
“AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction”