AI RESEARCH
Seer: Language Instructed Video Prediction with Latent Diffusion Models
arXiv CS.CV
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ArXi:2303.14897v4 Announce Type: replace Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis.