Search Results for author:
Found 39 papers, 19 papers with code
InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework
Third, to effectively train the framework, we construct a large-scale character dataset containing 10-million-level samples.
RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models
We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image.
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
We introduce a radiance representation that is both structured and fully explicit and thus greatly facilitates 3D generative modeling.
Correlation-Embedded Transformer Tracking: A Single-Branch Framework
Thus, we reformulate the two-branch Siamese tracking as a conceptually simple, fully transformer-based Single-Branch Tracking pipeline, dubbed SBT.
Plan, Posture and Go: Towards Open-World Text-to-Motion Generation
Some works use the CLIP model to align the motion space and the text space, aiming to enable motion generation from natural language motion descriptions.
VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder
The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net.
ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models
Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications.
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation.
GAIA: Zero-shot Talking Avatar Generation
Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image.
Multiple View Geometry Transformers for 3D Human Pose Estimation
In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation.
3D Human Pose Estimation
Multi-view 3D Human Pose Estimation
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
We introduce a highly performant 3D object detector for point clouds using the DETR framework.
Ranked #3 on
3D Object Detection
on ScanNetV2
3D Human Mesh Estimation from Virtual Markers
The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions.
Ranked #1 on
3D Human Pose Estimation
on Surreal
Human Pose as Compositional Tokens
Human pose is typically represented by a coordinate vector of body joints or their heatmap embeddings.
Ranked #1 on
Pose Estimation
on MPII Human Pose
Robust Multi-Object Tracking by Marginal Inference
To address the problem, we present an efficient approach to compute a marginal probability for each pair of objects in real time.
One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement
As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance.
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes.
Ranked #5 on
3D Multi-Person Pose Estimation
on Campus
VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data
While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked.
3D Multi-Person Pose Estimation (absolute)
3D Pose Estimation
Correlation-Aware Deep Tracking
In contrast to the Siamese-like feature extraction, our network deeply embeds cross-image feature correlation in multiple layers of the feature network.
Learning Tracking Representations via Dual-Branch Fully Transformer Networks
We present a Siamese-like Dual-branch network based on solely Transformers for tracking.
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark
This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments.
Ranked #2 on
Object Tracking
on MMPTRACK
Relational Self-Attention: What's Missing in Attention for Video Understanding
Convolution has been arguably the most important feature transform for modern neural networks, leading to the advance of deep learning.
Ranked #12 on
Action Recognition
on Diving-48
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild
We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint.
Ranked #7 on
3D Multi-Person Pose Estimation
on Campus
Context Modeling in 3D Human Pose Estimation: A Unified Perspective
By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results.
Ranked #65 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
A Multi-task Joint Framework for Real-time Person Search
To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively.
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations.
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views.
Ranked #1 on
3D Human Pose Estimation
on Total Capture
VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space.
Ranked #6 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Ranked #1 on
Multi-Object Tracking
on 2DMOT15
(using extra training data)
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation
Cross view feature fusion is the key to address the occlusion problem in human pose estimation.
Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
Then we lift the multi-view 2D poses to the 3D space by an Orientation Regularized Pictorial Structure Model (ORPSM) which jointly minimizes the projection error between the 3D and 2D poses, along with the discrepancy between the 3D pose and IMU orientations.
Ranked #1 on
3D Absolute Human Pose Estimation
on Total Capture
Cross View Fusion for 3D Human Pose Estimation
It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses.
Ranked #6 on
3D Human Pose Estimation
on Total Capture
Online Dictionary Learning for Approximate Archetypal Analysis
The archetypes generally correspond to the extremal points in the dataset and are learned by requiring them to be convex combinations of the training data.
Video Object Segmentation by Learning Location-Sensitive Embeddings
We address the problem of video object segmentation which outputs the masks of a target object throughout a video given only a bounding box in the first frame.
Object Detection in Videos by High Quality Object Linking
In particular, our method improves results by 8. 8% over the static image detector for fast moving objects.
Mining 3D Key-Pose-Motifs for Action Recognition
Recognizing an action from a sequence of 3D skeletal poses is a challenging task.
Representing Data by a Mixture of Activated Simplices
We show that under this restriction, building a model with simplices amounts to constructing a convex hull inside the sphere whose boundary facets is close to the data.
Robust Estimation of 3D Human Poses from a Single Image
We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons.
Ranked #29 on
3D Human Pose Estimation
on HumanEva-I
An Approach to Pose-Based Action Recognition
We start by improving a state of the art method for estimating human joint locations from videos.
