Search Results for author:
Found 34 papers, 13 papers with code
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback
Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate.
Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels
Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator.
High-Quality 3D Face Reconstruction with Affine Convolutional Networks
In our method, an affine transformation matrix is learned from the affine convolution layer for each spatial location of the feature maps.
Retrieval-Augmented Text-to-Audio Generation
Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art models, such as AudioLDM, trained on datasets with an imbalanced class distribution, such as AudioCaps, are biased in their generation performance.
Ranked #2 on
Audio Generation
on AudioCaps
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model.
Ranked #3 on
Audio Generation
on AudioCaps
Separate Anything You Describe
In this work, we introduce AudioSep, a foundation model for open-domain audio source separation with natural language queries.
WavJourney: Compositional Audio Creation with Large Language Models
Subjective evaluations demonstrate the potential of WavJourney in crafting engaging storytelling audio content from text.
Text-Driven Foley Sound Generation With Latent Diffusion Model
We have observed that the feature embedding extracted by the text encoder can significantly affect the performance of the generation model.
Leveraging Pre-trained AudioLDM for Text to Sound Generation: A Benchmark Study
Deep neural networks have recently achieved breakthroughs in sound generation with text prompts.
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency.
Ranked #9 on
Audio Generation
on AudioCaps
SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines
To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works.
Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields
In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images.
ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach
Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters.
TransVOS: Video Object Segmentation with Transformers
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS).
Single-Shot Motion Completion with Transformer
Motion completion is a challenging and long-discussed problem, which is of great significance in film and game applications.
Ranked #2 on
Motion Synthesis
on LaFAN1
One-shot Face Reenactment Using Appearance Adaptive Normalization
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.
In-game Residential Home Planning via Visual Context-aware Global Relation Learning
In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex.
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation
In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance.
MeInGame: Create a Game Character Face from a Single Portrait
To tackle these problems, we propose 1) a low-cost facial texture acquisition method, 2) a shape transfer algorithm that can transform the shape of a 3DMM mesh to games, and 3) a new pipeline for training 3D game face reconstruction networks.
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram
This paper studies an interesting question that whether a deep CNN can be trained to recover the depth behind an autostereogram and understand its content.
HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
To obtainmore accurate depth estimation in large gradient regions, itis necessary to obtain high-resolution features with spatialand semantic information.
Ranked #7 on
Unsupervised Monocular Depth Estimation
on KITTI-C
Stylized Neural Painting
Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering.
Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation
Previous works of this topic consider music-to-dance as a supervised motion generation problem based on time-series data.
Dynamic Future Net: Diversified Human Motion Generation
We evaluate our model on a wide range of motions and compare it with the state-of-the-art methods.
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer
Facial action unit (AU) intensity is an index to describe all visually discernible facial movements.
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks
In this paper, we introduce a method for one-shot face reenactment, which uses the reconstructed 3D meshes (i. e., the source mesh and driving mesh) as guidance to learn the optical flow needed for the reenacted face synthesis.
Neutral Face Game Character Auto-Creation via PokerFace-GAN
Besides, the neural network based renderer used in previous methods is also difficult to be extended to multi-view rendering cases.
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation
With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using default templates.
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
Deep implicit field regression methods are effective for 3D reconstruction from single-view images.
Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization
The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image.
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.
Face-to-Parameter Translation for Game Character Auto-Creation
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks
We propose an end to end deep learning approach for generating real-time facial animation from just audio.
