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Found 36 papers, 18 papers with code
SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation
To exclude the influence of algorithmic design, we base our experiments on two minimalist architectures: SMPLer-X, which consists of an intermediate step for hand and face localization, and SMPLest-X, an even simpler version that reduces the network to its bare essentials and highlights significant advances in the capture of articulated hands.
RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse
While motion generation has made substantial progress, its practical application remains constrained by dataset diversity and scale, limiting its ability to handle out-of-distribution scenarios.
PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval
We present PtychoFormer, a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval.
Flow Matching for Optimal Reaction Coordinates of Biomolecular System
We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics.
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
Crowd Motion Generation is essential in entertainment industries such as animation and games as well as in strategic fields like urban simulation and planning.
Ranked #1 on
Motion Generation
on KIL-ML
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems.
BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models
From the optimization perspective, a Low-rank Representation Mimicking (LRM) is applied to assist the optimization of binarized DMs.
Large Motion Model for Unified Multi-Modal Motion Generation
In this work, we present Large Motion Model (LMM), a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model.
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
Most work on learning from noisy labels has focused on standard loss-based performance measures.
InsActor: Instruction-driven Physics-based Characters
Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications.
FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
Notably, FineMoGen further enables zero-shot motion editing capabilities with the aid of modern large language models (LLM), which faithfully manipulates motion sequences with fine-grained instructions.
Ranked #6 on
Motion Synthesis
on KIT Motion-Language
Digital Life Project: Autonomous 3D Characters with Social Intelligence
In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment.
Ranked #3 on
Motion Synthesis
on InterHuman
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation
1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle.
Ranked #2 on
3D Human Pose Estimation
on UBody
On the Minimax Regret in Online Ranking with Top-k Feedback
In online ranking, a learning algorithm sequentially ranks a set of items and receives feedback on its ranking in the form of relevance scores.
PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds
To tackle these challenges, we propose a principled framework, PointHPS, for accurate 3D HPS from point clouds captured in real-world settings, which iteratively refines point features through a cascaded architecture.
ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model
However, the performance on more diverse motions remains unsatisfactory.
Ranked #2 on
Motion Synthesis
on KIT Motion-Language
BiBench: Benchmarking and Analyzing Network Binarization
Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width.
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
Instead of a deterministic language-motion mapping, MotionDiffuse generates motions through a series of denoising steps in which variations are injected.
Ranked #25 on
Motion Synthesis
on KIT Motion-Language
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
Balanced MSE for Imbalanced Visual Regression
Data imbalance exists ubiquitously in real-world visual regressions, e. g., age estimation and pose estimation, hurting the model's generalizability and fairness.
BiBERT: Accurate Fully Binarized BERT
The large pre-trained BERT has achieved remarkable performance on Natural Language Processing (NLP) tasks but is also computation and memory expensive.
Playing for 3D Human Recovery
Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios.
REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays
Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.
Bayesian Imbalanced Regression Debiasing
Compared to imbalanced and long-tailed classification, imbalanced regression has its unique challenges as the regression label space can be continuous, boundless, and high-dimensional.
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem.
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
By comprehensively investigating these GE-ViTs and comparing with their corresponding CNN models, we observe: 1) For the enhanced model, larger ViTs still benefit more for the OOD generalization.
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals
Real-world applications often require making use of {\em a range of incidental supervision signals}.
Towards Overcoming False Positives in Visual Relationship Detection
We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e. g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals.
REFINE: Prediction Fusion Network for Panoptic Segmentation
Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results.
Ranked #11 on
Panoptic Segmentation
on COCO test-dev
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
When H is the class of linear models, the class F consists of certain piecewise linear scoring functions that are characterized by the same number of parameters as in the linear case, and minimization over which can be performed using an adaptation of the min-pooling idea from neural network training.
BiPointNet: Binary Neural Network for Point Clouds
To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds.
Convex Calibrated Surrogates for the Multi-Label F-Measure
In particular, the F-measure explicitly balances recall (fraction of active labels predicted to be active) and precision (fraction of labels predicted to be active that are actually so), both of which are important in evaluating the overall performance of a multi-label classifier.
Foreseeing the Benefits of Incidental Supervision
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
Efficient Attention: Attention with Linear Complexities
Dot-product attention has wide applications in computer vision and natural language processing.
Ranked #2 on
Extractive Text Summarization
on GovReport
