LivePhoto: Real Image Animation with Text-guided Motion Control
Xi Chen Zhiheng Liu Mengting Chen Yutong Feng Yu Liu Yujun Shen Hengshuang Zhao
Despite the recent progress in text-to-video generation, existing studies
usually overlook the issue that only spatial contents but not temporal motions
in synthesized videos are under the control of text. Towards such a challenge,
this work presents a practical system, named LivePhoto, which allows users to
animate an image of their interest with text descriptions. We first establish a
strong baseline that helps a well-learned text-to-image generator (i.e., Stable
Diffusion) take an image as a further input. We then equip the improved
generator with a motion module for temporal modeling and propose a carefully
designed training pipeline to better link texts and motions. In particular,
considering the facts that (1) text can only describe motions roughly (e.g.,
regardless of the moving speed) and (2) text may include both content and
motion descriptions, we introduce a motion intensity estimation module as well
as a text re-weighting module to reduce the ambiguity of text-to-motion
mapping. Empirical evidence suggests that our approach is capable of well
decoding motion-related textual instructions into videos, such as actions,
camera movements, or even conjuring new contents from thin air (e.g., pouring
water into an empty glass). Interestingly, thanks to the proposed intensity
learning mechanism, our system offers users an additional control signal (i.e.,
the motion intensity) besides text for video customization.