9 citations · 17 across the 9 of their papers we have counts for
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cs.CV2026
FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
Kevin Qu, Tao Sun, Massimiliano Viola +5
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer…
cs.CV2026
FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
Yuchen Rao, Xuqian Ren, Yinyu Nie +4
Recovering complete 3D representations of objects from few casual image captures remains a significant challenge. Recent 3D generative models, particularly those based on Flow-Matc…
cs.CV2026
CoPE-VideoLM: Leveraging Codec Primitives For Efficient Video Language Modeling
Sayan Deb Sarkar, Rémi Pautrat, Ondrej Miksik +4
Video Language Models (VideoLMs) enable AI systems to understand temporal dynamics in videos. To fit within the maximum context window constraint, current methods use keyframe samp…