Event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. However, obtaining large-scale labeled ground-truth data for event-based vision tasks remains challenging and costly, which hinders the development of the event-based algorithm. In this paper, we present ControlEvents, a diffusion-based generative model designed to synthesize unlimited high-quality event data guided by diverse control signals such as class text labels, 2D skeletons, and 3D body poses. Our key insight is to leverage the diffusion prior from foundation models, such as Stable Diffusion, enabling high-quality event data generation with minimal fine-tuning and limited labeled data. Our method streamlines the data generation process and significantly reduces the cost of producing labeled event datasets. We demonstrate the effectiveness of our approach by synthesizing event data for visual recognition, 2D skeleton estimation, and 3D body pose estimation. Our experiments show that the synthesized labeled event data enhances model performance in all tasks. Additionally, our approach can generate events based on unseen text labels during training, illustrating the powerful text-based generation capabilities inherited from foundation models.

Reconstructing metrically accurate humans and their surrounding scenes from a single image is crucial for virtual reality, robotics, and comprehensive 3D scene understanding. However, existing methods struggle with depth ambiguity, occlusions, and physically inconsistent contacts. To address these challenges, we introduce PhySIC, a unified framework for physically plausible Human–Scene Interaction and Contact reconstruction. PhySIC recovers metrically consistent SMPL-X human meshes, dense scene surfaces, and vertex-level contact maps within a shared coordinate frame, all from a single RGB image. Starting from coarse monocular depth and parametric body estimates, PhySIC performs occlusion-aware inpainting, fuses visible depth with unscaled geometry for a robust initial metric scene scaffold, and synthesizes missing support surfaces like floors. A confidence-weighted optimization subsequently refines body pose, camera parameters, and global scale by jointly enforcing depth alignment, contact priors, interpenetration avoidance, and 2D reprojection consistency. Explicit occlusion masking safeguards invisible body regions against implausible configurations. PhySIC is highly efficient, requiring only 9 seconds for a joint human-scene optimization and less than 27 seconds for end-to-end reconstruction process. Moreover, the framework naturally handles multiple humans, enabling reconstruction of diverse human scene interactions. Empirically, PhySIC substantially outperforms single-image baselines, reducing mean per-vertex scene error from 641 mm to 227 mm, halving the pose-aligned mean per-joint position error (PA-MPJPE) to 42 mm, and improving contact F1-score from 0.09 to 0.51. Qualitative results demonstrate that PhySIC yields realistic foot-floor interactions, natural seating postures, and plausible reconstructions of heavily occluded furniture. By converting a single image into a physically plausible 3D human-scene pair, PhySIC advances accessible and scalable 3D scene understanding. Our implementation is publicly available at https://yuxuan-xue.com/physic.

Generating realistic and controllable 3D human avatars is a long-standing challenge. The difficulty increases when covering a broad range of attributes such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in both scale and diversity. The central question we address in this paper is: Can we distill existing foundation models to generate theoretically unbounded richly annotated 3D human data? We introduce InfiniHuman, a novel framework to distill these models synergistically, to generate richly annotated human data with minimal cost and theoretically unlimited scalability. Specifically, we propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. Remarkably, users cannot distinguish our automatically generated identities from scan renderings. InfiniHumanData contains 111K identities and covers unprecedented diversity in ethnicity, age, clothing styles, and more. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body shape parameters. Based on this, we learn InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate that InfiniHuman significantly surpasses existing state-of-the-art methods in terms of visual quality, generation speed, and controllability. Importantly, our approach democratizes high-quality avatar generation with fine-grained control at infinite scale through a practical and affordable solution. To facilitate future research, we will publicly release our automatic data generation pipeline and the comprehensive dataset InfiniHumanData, and the generative models InfiniHumanGen. The code and data of InfiniHuman is publicly available at https://yuxuan-xue.com/infini-human.

Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in computer vision and natural language. The results validate the effectiveness of BOFT as a generic finetuning method.

Creating realistic avatars from a single RGB image is an attractive yet challenging problem. To deal with challenging loose clothing or occlusion by interaction objects, we leverage powerful shape prior from 2D diffusion models pretrained on large datasets. Although 2D diffusion models demonstrate strong generalization capability, they cannot provide multi-view shape priors with guaranteed 3D consistency. We propose Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion. Our key insight is that 2D multi-view diffusion and 3D reconstruction models provide complementary information for each other. By coupling them in a tight manner, we can fully leverage the potential of both models. We introduce a novel image-conditioned generative 3D Gaussian Splats reconstruction model that leverages the prior from 2D multi-view diffusion models, and provides an explicit 3D representation, which further guides the 2D reverse sampling process to have better 3D consistency. Experiments show that our proposed framework outperforms state-of-the-art methods and enables the creation of realistic avatars from a single RGB image, achieving high-fidelity in both geometry and appearance. Extensive ablations also validate the efficacy of our design, (1) multi-view 2D priors conditioning in generative 3D reconstruction and (2) consistency refinement of sampling trajectory via the explicit 3D representation. Our code and models are released at https://yuxuan-xue.com/human-3diffusion/.

Abstract Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In recent years, event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. In this paper, we propose a novel approach for reconstructing such deformations using event measurements. Under the assumption of a static background, where all events are generated by the motion, our approach estimates the deformation of objects from events generated at the object contour in a probabilistic optimization framework. It associates events to mesh faces on the contour and maximizes the alignment of the line of sight through the event pixel with the associated face. In experiments on synthetic and real data of human body motion, we demonstrate the advantages of our method over state-of-the-art optimization and learning-based approaches for reconstructing the motion of human arms and hands. In addition, we propose an efficient event stream simulator to synthesize realistic event data for human motion.

Obtaining personalized 3D animatable avatars from a monocular camera has several real world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very challenging to model dynamic and finegrained clothing deformations from such sparse data. Existing methods for modeling 3D humans from depth data have limitations in terms of computational efficiency, mesh coherency, and flexibility in resolution and topology. For instance, reconstructing shapes using implicit functions and extracting explicit meshes per frame is computationally expensive and cannot ensure coherent meshes across frames. Moreover, predicting per-vertex deformations on a predesigned human template with a discrete surface lacks flexibility in resolution and topology. To overcome these limitations, we propose a novel method 'NSF : Neural Surface Fields’ for modeling 3D clothed humans from monocular depth. NSF defines a neural field solely on the base surface which models a continuous and flexible displacement field. NSF can be adapted to the base surface with different resolution and topology without retraining at inference time. Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining. To foster research in this direction, we release our code in project page at: https://yuxuan-xue.com/nsf.

Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important open problem. To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We find that this property is crucial for preserving the semantic generation ability of text-to-image diffusion models. To improve finetuning stability, we further propose Constrained Orthogonal Finetuning (COFT) which imposes an additional radius constraint to the hypersphere. Specifically, we consider two important finetuning text-to-image tasks: subject-driven generation where the goal is to generate subject-specific images given a few images of a subject and a text prompt, and controllable generation where the goal is to enable the model to take in additional control signals. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed.

This paper proposes a novel dictionary learning approach to detect event anomalities using skeletal information extracted from RGBD video. The event action is represented as several latent action atoms and composed of latent spatial and temporal attributes. We aim to construct a network able to learn from few examples and also rules defined by the user. The skeleton frames are clustered by an initial K-means method. Each skeleton frame is assigned with a varying weight parameter and fed into our Gradual Online Dictionary Learning (GODL) algorithm. During the training process, outlier frames will be gradually filtered by reducing the weight that is inversely proportional to a cost. To strictly distinguish the event action from similar actions and robustly acquire its action units, we build a latent unit temporal structure for each sub-action.We validate the method at the example of fall event detection on NTU RGB+D dataset, because it provides a benchmark available for comparison. We present the experimental validation of the achieved accuracy, recall, and precision. Our approach achieves the best performance in precision and accuracy of human fall event detection, compared with other existing dictionary learning methods. Our method remains the highest accuracy and the lowest variance, with increasing noise ratio.