Similarly, to control the pacing and flow of AI-generated videos, users should have control over the dynamics and composition of videos produced by generative models. To this end, numerous motion control methods [25, 33, 57, 59, 61, 63, 72] have been proposed to control moving object trajectories in videos generated by text-to-video (T2V) diffusion models [4, 17]. Motion customization, in particular, aims to control T2V diffusion models with the motion of a reference video [26, 31, 36, 71, 76]. With the assistance of the reference video, users are able to specify the desired object movements and camera framing in detail. Formally speaking, given a reference video, motion customization aims to adjust a pre-trained T2V diffusion model, so the output videos sampled from the adjusted model follow the object movements and camera framing of the reference video (see Figure 1 for an example). Given that motion is a high-level concept involving both spatial and temporal dimensions [65, 71], motion customization is considered a non-trivial task. Recently, many motion customization methods have been proposed to eliminate the influence of visual appearance in the reference video. Among them, a standout strategy is fine-tuning the pre-trained T2V diffusion model to reconstruct the frame differences of the reference video.

Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves the model ability in generating images for non-target concepts. In this paper, we propose Reliable Concept Erasing via Lightweight Erasers (Receler), which learns a lightweight Eraser to perform concept erasing and enhances locality and robustness with the proposed concept-localized regularization and adversarial prompt learning, respectively. Comprehensive quantitative and qualitative experiments with various concept prompts verify the superiority of Receler over the previous erasing methods on the above two desirable properties.