Seer: Language Instructed Video Prediction with Latent Diffusion Models

• URL: http://arxiv.org/abs/2303.14897v3
• Date: Mon, 29 Jan 2024 03:18:25 GMT
• Title: Seer: Language Instructed Video Prediction with Latent Diffusion Models
• Authors: Xianfan Gu, Chuan Wen, Weirui Ye, Jiaming Song, Yang Gao
• Abstract summary: Text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. We propose a sample and computation-efficient model, named textbfSeer, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames.
• Score: 43.708550061909754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We enhance the U-Net and language conditioning model by incorporating computation-efficient spatial-temporal attention. Furthermore, we introduce a novel Frame Sequential Text Decomposer module that dissects a sentence's global instruction into temporally aligned sub-instructions, ensuring precise integration into each frame of generation. Our framework allows us to effectively leverage the extensive prior knowledge embedded in pretrained T2I models across the frames. With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2), Bridgedata and EpicKitchens-100 datasets demonstrate our superior video prediction performance with around 480-GPU hours versus CogVideo with over 12,480-GPU hours: achieving the 31% FVD improvement compared to the current SOTA model on SSv2 and 83.7% average preference in the human evaluation.

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