Recent diffusion models have made significant advancements in generating lifelike videos from driving signals, including a reference character and a skeleton sequence. Nevertheless, these models often struggle with maintaining fidelity, as the generated results frequently deviate in character features, e.g., appearance and identity from the reference. We attribute this issue to the use of driving signals from the same individual during the training process, which biases the model towards skeleton-based shape features and limits its capacity to fully exploit character-specific information, and propose DreamHA to address this issue. DreamHA incorporates diffusion models with Rigid Transformation Augmentation (RTAug), a simple yet effective technique to perturb the shape characteristics of training data, improving the capability of diffusion models in capturing basic appearance features. Additionally, we introduce Identity Keeper (IK) to provide fine-grained facial control and enhance identity consistency. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches, producing more faithful and consistent animations.

Multi-view Diffusion has greatly advanced the development of 3D content creation by generating multiple images from distinct views, achieving remarkable photorealistic results. However, existing works are still vulnerable to inconsistent 3D geometric structures (commonly known as Janus Problem) and severe artifacts. In this paper, we introduce MIRROR, a versatile plug-and-play method that rectifies such inconsistencies in a training-free manner, enabling the acquisition of high-fidelity, realistic structures without compromising diversity. Our key idea focuses on tracing the motion trajectory of physical points across adjacent viewpoints, enabling rectifications based on neighboring observations of the same region. Technically, MIRROR comprises two core modules: Trajectory Tracking Module (TTM) for pixel-wise trajectory tracking that labels identical points across views, and Feature Rectification Module (FRM) for explicitly adjustment of each pixel embedding on noisy synthesized images by minimizing the distance to corresponding block features in neighboring views, thereby achieving consistent outputs. Extensive evaluations demonstrate that MIRROR can seamlessly integrate with a diverse range of off-the-shelf object-level multi-view diffusion models, significantly enhancing both the consistency and the fidelity in an efficient way.