Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns

We propose Lodge++, a choreography framework to generate high-quality, ultra-long, and vivid dances given the music and desired genre. To handle the challenges in computational efficiency, the learning of complex and vivid global choreography patterns, and the physical quality of local dance movements, Lodge++ adopts a two-stage strategy to produce dances from coarse to fine. In the first stage, a global choreography network is designed to generate coarse-grained dance primitives that capture complex global choreography patterns. In the second stage, guided by these dance primitives, a primitive-based dance diffusion model is proposed to further generate high-quality, long-sequence dances in parallel, faithfully adhering to the complex choreography patterns. Additionally, to improve the physical plausibility, Lodge++ employs a penetration guidance module to resolve character self-penetration, a foot refinement module to optimize foot-ground contact, and a multi-genre discriminator to maintain genre consistency throughout the dance. Lodge++ is validated by extensive experiments, which show that our method can rapidly generate ultra-long dances suitable for various dance genres, ensuring well-organized global choreography patterns and high-quality local motion.

Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives

We propose Lodge, a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture, and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion, which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast, the second-stage is the local diffusion, which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition, we propose a Foot Refine Block to optimize the contact between the feet and the ground, enhancing the physical realism of the motion. Our approach can parallelly generate dance sequences of extremely long length, striking a balance between global choreographic patterns and local motion quality and expressiveness. Extensive experiments validate the efficacy of our method.

MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models

Gesture synthesis is a vital realm of human-computer interaction, with wide-ranging applications across various fields like film, robotics, and virtual reality. Recent advancements have utilized the diffusion model and attention mechanisms to improve gesture synthesis. However, due to the high computational complexity of these techniques, generating long and diverse sequences with low latency remains a challenge. We explore the potential of state space models (SSMs) to address the challenge, implementing a two-stage modeling strategy with discrete motion priors to enhance the quality of gestures. Leveraging the foundational Mamba block, we introduce MambaTalk, enhancing gesture diversity and rhythm through multimodal integration. Extensive experiments demonstrate that our method matches or exceeds the performance of state-of-the-art models.

Harmonious Group Choreography with Trajectory-Controllable Diffusion

Creating group choreography from music has gained attention in cultural entertainment and virtual reality, aiming to coordinate visually cohesive and diverse group movements. Despite increasing interest, recent works face challenges in achieving aesthetically appealing choreography, primarily for two key issues: multi-dancer collision and single-dancer foot slide. To address these issues, we propose a Trajectory-Controllable Diffusion (TCDiff), a novel approach that harnesses non-overlapping trajectories to facilitate coherent dance movements. Specifically, to tackle dancer collisions, we introduce a Dance-Beat Navigator capable of generating trajectories for multiple dancers based on the music, complemented by a Distance-Consistency loss to maintain appropriate spacing among trajectories within a reasonable threshold. To mitigate foot sliding, we present a Footwork Adaptor that utilizes trajectory displacement from adjacent frames to enable flexible footwork, coupled with a Relative Forward-Kinematic loss to adjust the positioning of individual dancers' root nodes and joints. Extensive experiments demonstrate that our method achieves state-of-the-art results.

CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion

Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. Firstly, users struggle to craft textual prompts that meticulously detail visual elements of the input image. Secondly, prevalent models, when effecting modifications in specific zones, frequently disrupt the overall artistic style, complicating the attainment of cohesive and aesthetically unified artworks. To surmount these obstacles, we build the innovative unified framework CreativeSynth, which is based on a diffusion model with the ability to coordinate multimodal inputs and multitask in the field of artistic image generation. By integrating multimodal features with customized attention mechanisms, CreativeSynth facilitates the importation of real-world semantic content into the domain of art through inversion and real-time style transfer. This allows for the precise manipulation of image style and content while maintaining the integrity of the original model parameters. Rigorous qualitative and quantitative evaluations underscore that CreativeSynth excels in enhancing artistic images' fidelity and preserves their innate aesthetic essence. By bridging the gap between generative models and artistic finesse, CreativeSynth becomes a custom digital palette.

Exploring Multi-Modal Control in Music-Driven Dance Generation

Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process. To address these issues, we propose a unified framework capable of generating high-quality dance movements and supporting multi-modal control, including genre control, semantic control, and spatial control. First, we decouple the dance generation network from the dance control network, thereby avoiding the degradation in dance quality when adding additional control information. Second, we design specific control strategies for different control information and integrate them into a unified framework. Experimental results show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and controllability.

Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute

Generating 3D human models directly from text helps reduce the cost and time of character modeling. However, achieving multi-attribute controllable and realistic 3D human avatar generation is still challenging due to feature coupling and the scarcity of realistic 3D human avatar datasets. To address these issues, we propose Text2Avatar, which can generate realistic-style 3D avatars based on the coupled text prompts. Text2Avatar leverages a discrete codebook as an intermediate feature to establish a connection between text and avatars, enabling the disentanglement of features. Furthermore, to alleviate the scarcity of realistic style 3D human avatar data, we utilize a pre-trained unconditional 3D human avatar generation model to obtain a large amount of 3D avatar pseudo data, which allows Text2Avatar to achieve realistic style generation. Experimental results demonstrate that our method can generate realistic 3D avatars from coupled textual data, which is challenging for other existing methods in this field.

Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control

This study aims to improve the generation of 3D gestures by utilizing multimodal information from human speech. Previous studies have focused on incorporating additional modalities to enhance the quality of generated gestures. However, these methods perform poorly when certain modalities are missing during inference. To address this problem, we suggest using speech-derived multimodal priors to improve gesture generation. We introduce a novel method that separates priors from speech and employs multimodal priors as constraints for generating gestures. Our approach utilizes a chain-like modeling method to generate facial blendshapes, body movements, and hand gestures sequentially. Specifically, we incorporate rhythm cues derived from facial deformation and stylization prior based on speech emotions, into the process of generating gestures. By incorporating multimodal priors, our method improves the quality of generated gestures and eliminate the need for expensive setup preparation during inference. Extensive experiments and user studies confirm that our proposed approach achieves state-of-the-art performance.

Magic: Multi Art Genre Intelligent Choreography Dataset and Network for 3D Dance Generation

Achieving multiple genres and long-term choreography sequences from given music is a challenging task, due to the lack of a multi-genre dataset. To tackle this problem,we propose a Multi Art Genre Intelligent Choreography Dataset (MagicDance). The data of MagicDance is captured from professional dancers assisted by motion capture technicians. It has a total of 8 hours 3D motioncapture human dances with paired music, and 16 different dance genres. To the best of our knowledge, MagicDance is the 3D dance dataset with the most genres. In addition, we find that the existing two types of methods (generation-based method and synthesis-based method) can only satisfy one of the diversity and duration, but they can complement to some extent. Based on this observation, we also propose a generation-synthesis choreography network (MagicNet), which cascades a Diffusion-based 3D Diverse Dance fragments Generation Network (3DGNet) and a Genre&Coherent aware Retrieval Module (GCRM). The former can generate various dance fragments from only one music clip. The latter is utilized to select the best dance fragment generated by 3DGNet and switch them into a complete dance according to the genre and coherent matching score. Quantitative and qualitative experiments demonstrate the quality of MagicDance, and the state-of-the-art performance of MagicNet.

Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition

Sign language is a beautiful visual language and is also the primary language used by speaking and hearing-impaired people. However, sign language has many complex expressions, which are difficult for the public to understand and master. Sign language recognition algorithms will significantly facilitate communication between hearing-impaired people and normal people. Traditional continuous sign language recognition often uses a sequence learning method based on Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). These methods can only learn spatial and temporal features separately, which cannot learn the complex spatial-temporal features of sign language. LSTM is also difficult to learn long-term dependencies. To alleviate these problems, this paper proposes a multi-view spatial-temporal continuous sign language recognition network. The network consists of three parts. The first part is a Multi-View Spatial-Temporal Feature Extractor Network (MSTN), which can directly extract the spatial-temporal features of RGB and skeleton data; the second is a sign language encoder network based on Transformer, which can learn long-term dependencies; the third is a Connectionist Temporal Classification (CTC) decoder network, which is used to predict the whole meaning of the continuous sign language. Our algorithm is tested on two public sign language datasets SLR-100 and PHOENIX-Weather 2014T (RWTH). As a result, our method achieves excellent performance on both datasets. The word error rate on the SLR-100 dataset is 1.9%, and the word error rate on the RWTHPHOENIX-Weather dataset is 22.8%.