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Found 32 papers, 11 papers with code
Taming Diffusion Probabilistic Models for Character Control
We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals.
Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness
To tackle these issues, we introduce FreeTalker, which, to the best of our knowledge, is the first framework for the generation of both spontaneous (e. g., co-speech gesture) and non-spontaneous (e. g., moving around the podium) speaker motions.
HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback
We introduce HuTuMotion, an innovative approach for generating natural human motions that navigates latent motion diffusion models by leveraging few-shot human feedback.
Realistic Human Motion Generation with Cross-Diffusion Models
Our method integrates 3D and 2D information using a shared transformer network within the training of the diffusion model, unifying motion noise into a single feature space.
SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark
We present SignAvatars, the first large-scale, multi-prompt 3D sign language (SL) motion dataset designed to bridge the communication gap for Deaf and hard-of-hearing individuals.
SemanticBoost: Elevating Motion Generation with Augmented Textual Cues
Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD).
TapMo: Shape-aware Motion Generation of Skeleton-free Characters
In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters.
LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation
While previous methods are able to generate speech rhythm-synchronized gestures, the semantic context of the speech is generally lacking in the gesticulations.
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer
On the other hand, different from the vanilla version, we adopt a learnable scaling operation on content features before content-style feature interaction, which better preserves the original similarity between a pair of content features while ensuring the stylization quality.
Learning Anchor Transformations for 3D Garment Animation
Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning.
BoPR: Body-aware Part Regressor for Human Shape and Pose Estimation
This paper presents a novel approach for estimating human body shape and pose from monocular images that effectively addresses the challenges of occlusions and depth ambiguity.
Ranked #16 on
3D Human Pose Estimation
on 3DPW
HMC: Hierarchical Mesh Coarsening for Skeleton-free Motion Retargeting
We present a simple yet effective method for skeleton-free motion retargeting.
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry
Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing.
ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction
Our method significantly outperforms the best interacting-hand approaches on the InterHand2. 6M dataset while yielding comparable performance with the state-of-the-art single-hand methods on the FreiHand dataset.
Ranked #2 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
Ranked #11 on
Motion Synthesis
on HumanML3D
Harmonious Feature Learning for Interactive Hand-Object Pose Estimation
Notably, the performance of our model on hand pose estimation even surpasses that of existing works that only perform the single-hand pose estimation task.
Ranked #2 on
hand-object pose
on DexYCB
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation.
Towards Hard-Positive Query Mining for DETR-based Human-Object Interaction Detection
Specifically, we shift the GT bounding boxes of each labeled human-object pair so that the shifted boxes cover only a certain portion of the GT ones.
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data
We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem.
Geometric Structure Preserving Warp for Natural Image Stitching
Further, an optimized GES energy term is presented to reasonably determine the weights of the sampling points on the geometric structure, and the term is added into the Global Similarity Prior (GSP) stitching model called GES-GSP to achieve a smooth transition between local alignment and geometric structure preservation.
PONet: Robust 3D Human Pose Estimation via Learning Orientations Only
Conventional 3D human pose estimation relies on first detecting 2D body keypoints and then solving the 2D to 3D correspondence problem. Despite the promising results, this learning paradigm is highly dependent on the quality of the 2D keypoint detector, which is inevitably fragile to occlusions and out-of-image absences. In this paper, we propose a novel Pose Orientation Net (PONet) that is able to robustly estimate 3D pose by learning orientations only, hence bypassing the error-prone keypoint detector in the absence of image evidence.
Ranked #80 on
3D Human Pose Estimation
on MPI-INF-3DHP
Structure-Aware Feature Generation for Zero-Shot Learning
In this paper, we introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to explicitly account for the topological structure in learning both the latent space and the generative networks.
Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-Grained Recognition
Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability.
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition.
Ranked #31 on
Fine-Grained Image Classification
on CUB-200-2011
An End-to-end Framework for Unconstrained Monocular 3D Hand Pose Estimation
This work addresses the challenging problem of unconstrained 3D hand pose estimation using monocular RGB images.
Learning Multi-level Weight-centric Features for Few-shot Learning
Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning.
Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts
We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated.
Context Refinement for Object Detection
Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions.
A Coarse-Fine Network for Keypoint Localization
To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers.
Real Time Fine-Grained Categorization with Accuracy and Interpretability
The proposed architecture consists of a part localization network, a two-stream classification network that simultaneously encodes object-level and part-level cues, and a feature vectors fusion component.
Part-Stacked CNN for Fine-Grained Visual Categorization
In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy.
Ranked #62 on
Fine-Grained Image Classification
on CUB-200-2011
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization
We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework.
