AI Research Paper Accepted & Published at ECCV 2024

We are thrilled to announce that our research paper "Scalable Group Choreography via Variational Phase Manifold Learning" has been accepted and published at the European Conference on Computer Vision @eccvconf 2024!

This research, presented by the AIOZ AI Team in Milan, led to the development of a generative model that solves the scalability challenge in group dance generation while preserving naturalness and synchronization.

Let's further analyze this research below:

Existing group dance generation models typically face limitations in scalability. They either generate dances for a fixed number of performers or suffer from high memory consumption due to architectural constraints.

These approaches - often based on collaborative mechanisms such as cross-entity or global attention - are restricted to a predetermined number of dancers, severely limiting their application in real-world scenarios requiring larger group choreographies.

To address these challenges, we proposed a novel approach to scalable group dance generation using a phase-based variational generative model known as Phase-conditioned Dance VAE (PDVAE).

Our method achieves high-fidelity group dance motion and enables the generation of realistic and synchronized group dance performances without increasing computational and memory costs, even as the number of dancers grows.

The intensive experiments we carried out on two public datasets prove that our proposed method outperforms recent state-of-the-art approaches by a large margin and is scalable to accommodate a greater number of dancers beyond the training data.

Based on this, we can safely conclude that PDVAE provides a flexible and efficient solution for generating crowd-scale dance animations, with potential applications in diverse fields such as entertainment, virtual reality, education, and media production.

If you would like to dive deeper into this research work, you can refer to the links below:

▪️ Paper: https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02766.pdf

▪️ Project Page: https://aioz-ai.github.io/VAE/

▪️ Poster: https://eccv2024.ecva.net/virtual/2024/poster/1394

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