Mixed Reality (MR) headsets promise a future of immersive telepresence where virtual humans blend indistinguishably into their real or virtual surroundings. Achieving this vision requires a method capable of capturing a user's motion, estimating their appearance under novel lighting, and understanding the surrounding environment - all from the constrained viewpoint of a head-mounted display (HMD). Existing approaches treat these as isolated problems: they either focus on driving avatars with baked-in lighting or rely on complex studio setups for relighting. In this paper, we present EgoRelight , a holistic framework for egocentric telepresence that simultaneously captures full-body human performance, synthesizes photorealistic and relightable appearance, and estimates high dynamic range (HDR) environment maps from a single HMD. First, to ensure accurate motion and surface reconstruction, we propose an egocentric perception module that leverages stereo down-facing cameras to extract dense depth maps, which serve as robust geometric control signals to drive a mesh-based avatar. Second, we introduce a novel neural appearance model that learns to synthesize view-dependent specular and view-independent diffuse shading separately. By employing a specialized ray-sampling strategy, our model generalizes to unseen illumination without relying on restrictive analytical BRDF priors. Third, we enable seamless avatar integration into the physical world via a test-time inverse rendering process, which recovers an HDR environment map by matching the pre-trained avatar's appearance to the live egocentric camera observations. We demonstrate the efficiency of our system through a social telepresence application, where remote users are coherently relit according to their local physical environment. Extensive experiments show that our individual components and the integrated system significantly outperform the best combinations of state-of-the-art baselines in both geometric accuracy and rendering as well as relighting fidelity. Further details can be found on our project page 1 .

Human performance capture from RGB videos in unconstrained environments has become very popular for applications that require generating virtual avatars or digital actors. SOTA methods use neural network (NN) techniques to estimate the shape directly from photos, yielding a simplified model of the human body. While effective, NN techniques frequently fail under challenging poses and do not preserve temporal consistency. On the other hand, optimization-based methods like shape-from-silhouette can produce more precise reconstruction; however, they typically require a good initialization and are computationally more intensive than NN. To address issues of previous methods, this work proposes a learning-based approach for optimizing fine-grained shape representation from a monocular RGB video. Our main idea is to sequentially recover different shape details (i.e. average shape, clothing, wrinkles) using separate neural networks. At each level, our network takes the sparse/noisy gradients of body mesh vertices w.r.t. the shape, and predicts dense gradients to update the body shape. Despite being trained on synthetic data, these networks have surprisingly good generalization to real images. Experimental validation shows that our approach outperforms NN approaches in recovering shape details while also being an order of magnitude faster than optimization-based methods and robust across varied poses and novel views.

This paper concerns the undetermined problem of estimating geometric transformation between image pairs. Recent methods introduce deep neural networks to predict the controlling parameters of hand-crafted geometric transformation models (e.g. thin-plate spline) for image registration and matching. However, the low-dimension parametric models are incapable of estimating a highly complex geometric transform with limited flexibility to model the actual geometric deformation from image pairs. To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment. Arbicon-Net is generalized from training data to predict the desired arbitrary continuous geometric transformation in a data-driven manner for unseen new pair of images. Particularly, without imposing penalization terms, the predicted displacement vector function is proven to be spatially continuous and smooth. To verify the performance of Arbicon-Net, we conducted semantic alignment tests over both synthetic and real image dataset with various experimental settings. The results demonstrate that Arbicon-Net outperforms the previous image alignment techniques in identifying the image correspondences.