Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avatar generation methods, such as pre-trained diffusion models with score distillation sampling (SDS), which align avatars with pseudo ground-truth outputs from the diffusion model. However, directly distilling 4D avatars from video diffusion often leads to over-smooth results due to spatial and temporal inconsistencies in the generated video. To address this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial and temporal consistency dataset for 4D avatar reconstruction using the video diffusion model. Specifically, Zero-1-to-A iteratively constructs video datasets and optimizes animatable avatars in a progressive manner, ensuring that avatar quality increases smoothly and consistently throughout the learning process. This progressive learning involves two stages: (1) Spatial Consistency Learning fixes expressions and learns from front-to-side views, and (2) Temporal Consistency Learning fixes views and learns from relaxed to exaggerated expressions, generating 4D avatars in a simple-to-complex manner. Extensive experiments demonstrate that Zero-1-to-A improves fidelity, animation quality, and rendering speed compared to existing diffusion-based methods, providing a solution for lifelike avatar creation. Code is publicly available at: https://github.com/ZhenglinZhou/Zero-1-to-A.

Text-to-3D generation automates 3D content creation from textual descriptions, which offers transformative potential across various fields. However, existing methods often struggle to align generated content with human preferences, limiting their applicability and flexibility. To address these limitations, in this paper, we propose DreamDPO, an optimization-based framework that integrates human preferences into the 3D generation process, through direct preference optimization. Practically, DreamDPO first constructs pairwise examples, then validates their alignment with human preferences using reward or large multimodal models, and lastly optimizes the 3D representation with a preference-driven loss function. By leveraging relative preferences, DreamDPO reduces reliance on precise quality evaluations while enabling fine-grained controllability through preference-guided optimization. Experiments demonstrate that DreamDPO achieves state-of-the-art results, and provides higher-quality and more controllable 3D content compared to existing methods. The code and models will be open-sourced.

The rapid advancements in large foundation models have significantly transformed the field of visual content generation, impacting domains such as image synthesis, video generation, and 3D modeling. This tutorial will provide an in-depth exploration of the state-of-the-art techniques and methodologies used in visual content generation, emphasizing the role of large-scale generative models. The tutorial will cover fundamental principles, model architectures, recent breakthroughs, and practical applications. We will discuss various generative paradigms, including diffusion models, autoregressive models, and large multimodal models, highlighting their strengths and limitations. Additionally, we will delve into the challenges of controllability, personalization, and realism in generated content, along with open research problems and future directions. By the end of the tutorial, attendees will gain a comprehensive understanding of contemporary visual content generation techniques and their applications, equipping them with the knowledge to leverage these models in their research and projects.

Recently, deep learning-based facial landmark detection has achieved significant improvement. However, the semantic ambiguity problem degrades detection performance. Specifically, the semantic ambiguity causes inconsistent annotation and negatively affects the model's convergence, leading to worse accuracy and instability prediction. To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of se-mantic ambiguity. We find that semantic ambiguity results in the anisotropic predicted distribution, which inspires us to use predicted distribution to represent semantic ambiguity. Based on this, we design the STAR loss that measures the anisotropism of the predicted distribution. Compared with the standard regression loss, STAR loss is encouraged to be small when the predicted distribution is anisotropic and thus adaptively mitigates the impact of semantic ambiguity. Moreover, we propose two kinds of eigen-value restriction methods that could avoid both distribution's abnormal change and the model's premature convergence. Finally, the comprehensive experiments demonstrate that STAR loss outperforms the state-of-the-art methods on three benchmarks, i.e., COFW, 300W, and WFLW, with negligible computation overhead. Code is at https://github.com/ZhenglinZhou/STAR

Magnetic Resonance Imaging (MRI) is frequently used by physicians for diagnosing human tissue, which requires focusing on specific content regions. However, existing compressed-sensing MRI (CS-MRI) reconstruction methods do not optimize under-sampling adaptively based on content or utilize k-space sensing resources effectively. To address these issues, we propose CBRecNet, a model that combines under-sampling pattern optimization with content-based reconstruction network. We extract multi-scale features of the content and calibrate the features of the reconstruction network using a pixel attention mechanism, which can guide the optimizable pattern to get a more suitable sampling pattern. To validate the proposed CBRecNet, we conduct extensive experiments on the CHAOS dataset with a four-stage learning strategy and the result demonstrates a very favorable outcome in CS-MRI. Our model provides a new approach to calibrated feature schemes based on meaningful medical content and demonstrates promising applications in CS-MRI.