Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks80 from public data
- DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection648
The on-going effort of constructing a large- scale benchmark for face forgery detection is presented, with 60, 000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind.
- MEAD: A Large-Scale Audio-Visual Dataset for Emotional Talking-Face Generation522
An emotional talking-face generation baseline that enables the manipulation of both emotion and its intensity and could be used for a number of research projects including conditional generation, cross-modal understanding and expression recognition.
- Focal Frequency Loss for Image Reconstruction and Synthesis520
It is shown that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further, and a novel focal frequency loss is proposed, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones.
- Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation478
A unified and efficient Cross-modality Guided Encoder to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
- Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation474
This paper proposes a clean yet effective framework to generate pose-controllable talking faces whose poses are controllable by other videos and has multiple advanced capabilities including extreme view robustness and talking face frontalization.
- OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation469
OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects, is proposed, facilitating the development of 3D perception, reconstruction, and generation in the real world.
- MotionBERT: A Unified Perspective on Learning Human Motion Representations428
A pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations is proposed, which achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoding with a simple regression head, which demonstrates the versatility of the learned motion representations.
- TAM: Temporal Adaptive Module for Video Recognition388
A new temporal adaptive module (TAM) to generate video-specific temporal kernels based on its own feature map that outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity.
- Audio-Driven Emotional Video Portraits300
EVP is presented, a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios, using the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces.
- EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model250
The Emotion-Aware Motion Model (EAMM) is proposed to generate one-shot emotional talking faces by involving an emotion source video and an Implicit Emotion Displacement Learner to represent emotion-related facial dynamics as linearly additive displacements to the previously acquired motion representations.
- ReenactGAN: Learning to Reenact Faces via Boundary Transfer229
The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from an arbitrary person's monocular video input to a target person’s video, and can perform photo-realistic face reenactment.
- Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation177
Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF through two semantic-aware modules, and renders more realistic video portraits compared to previous methods.
- Everybody’s Talkin’: Let Me Talk as You Want172
A method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video, which is end-to-end learnable and robust to voice variations in the source audio.
- Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation169
This work proposes a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation, and develops a contrastive learning strategy based on audio-text alignment for better audio representations.
- 144
- Text2Human141
Extensive quantitative and qualitative evaluations demonstrate that the proposed Text2Human framework can generate more diverse and realistic human images compared to state-of-the-art methods.
- DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering131
This work presents DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering, and provides a large-scale and quantitative benchmark in full-scale, with multiple tasks to evaluate the existing progress of novel view synthesis, novel pose animation synthesis, and novel identity rendering methods.
- CelebV-Text: A Large-Scale Facial Text-Video Dataset129
The effectiveness and potential of CelebV- Text are shown through extensive self-evaluation, and a benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task.
- MonoHuman: Animatable Human Neural Field from Monocular Video122
A novel framework MonoHuman is proposed, which robustly renders view-consistent and high-fidelity avatars under arbitrary novel poses and is able to model the deformation field with bi-directional constraints and explicitly leverage the off-the-peg keyframe information to reason the feature correlations for coherent results.
- 122
- TransGaGa: Geometry-Aware Unsupervised Image-To-Image Translation114
A novel disentangle-and-translate framework to tackle the complex objects image-to-image translation task, which disentangles image space into a Cartesian product of the appearance and the geometry latent spaces and supports multimodal translation.
- Aggregation via Separation: Boosting Facial Landmark Detector With Semi-Supervised Style Translation95
A new perspective of facial landmark detection is investigated and it is demonstrated it leads to further notable improvement and surprisingly outperforms the fully-supervised model by a large margin.
- SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling83
A new synthetic dataset, SynBody, with three appealing features: a clothed parametric human model that can generate a diverse range of subjects; the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; and a scalable system for producing realistic data to facilitate real-world tasks.
- Text2Performer: Text-Driven Human Video Generation78
This work decomposes the VQVAE latent space into human appearance and pose representation in an unsupervised manner by utilizing the nature of human videos, and proposes continuous VQ-diffuser to sample a sequence of pose embeddings.
- Make a Face: Towards Arbitrary High Fidelity Face Manipulation78
This work proposes Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.