Text-driven Video Prediction

TL;DR

This paper introduces Text-driven Video Prediction, a new task that generates future video frames from an initial image and descriptive text, emphasizing the causal influence of text on motion and appearance.

Contribution

The paper proposes a novel framework with a Text Inference Module to leverage text for controlling motion in video prediction, addressing the lack of deterministic constraints in existing models.

Findings

Outperforms baseline models on Something-Something V2 dataset

Effectively utilizes text for causal motion inference

Produces coherent video sequences based on textual descriptions

Abstract

Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image, text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption separately. The key to addressing the TVP task depends on fully exploring the underlying motion information in text descriptions, thus facilitating plausible video generation. In fact, this task is intrinsically a cause-and-effect problem, as the text content directly influences the motion changes of frames. To investigate the capability of text in causal inference for progressive motion information, our TVP framework contains a Text Inference Module (TIM), producing step-wise embeddings to regulate motion inference for subsequent frames. In particular, a refinement mechanism incorporating global motion semantics guarantees coherent generation. Extensive experiments are conducted on Something-Something V2 and Single Moving MNIST datasets. Experimental results demonstrate that our model achieves better results over other baselines, verifying the effectiveness of the proposed framework.