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Found 21 papers, 5 papers with code
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction.
Drivable 3D Gaussian Avatars
We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats.
Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input
Clothing is an important part of human appearance but challenging to model in photorealistic avatars.
NPC: Neural Point Characters from Video
Previous methods avoid using a template but rely on a costly or ill-posed mapping from observation to canonical space.
RelightableHands: Efficient Neural Relighting of Articulated Hand Models
To achieve generalization, we condition the student model with physics-inspired illumination features such as visibility, diffuse shading, and specular reflections computed on a coarse proxy geometry, maintaining a small computational overhead.
Drivable Volumetric Avatars using Texel-Aligned Features
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.
Garment Avatars: Realistic Cloth Driving using Pattern Registration
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks
While a few such approaches exist, those have limited generalization capabilities and are prone to learning spurious (chance) correlations between irrelevant body parts, resulting in implausible deformations and missing body parts on unseen poses.
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
In this work, for the first time, we enable autoregressive modeling of implicit avatars.
Modeling Clothing as a Separate Layer for an Animatable Human Avatar
To address these difficulties, we propose a method to build an animatable clothed body avatar with an explicit representation of the clothing on the upper body from multi-view captured videos.
DeepMesh: Differentiable Iso-Surface Extraction
Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field.
Driving-Signal Aware Full-Body Avatars
The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.
Learning Compositional Radiance Fields of Dynamic Human Heads
In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.
Masksembles for Uncertainty Estimation
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
MeshSDF: Differentiable Iso-Surface Extraction
Unfortunately, these methods are often not suitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field.
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection
People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height.
Modeling Facial Geometry Using Compositional VAEs
We propose a method for learning non-linear face geometry representations using deep generative models.
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
We present a unified framework for understanding human social behaviors in raw image sequences.
Ranked #2 on
Action Recognition
on Volleyball
Principled Parallel Mean-Field Inference for Discrete Random Fields
Mean-field variational inference is one of the most popular approaches to inference in discrete random fields.
Probability Occupancy Maps for Occluded Depth Images
We propose a novel approach to computing the probabilities of presence of multiple and potentially occluding objects in a scene from a single depth map.
