Despite the recent advances in the so-called "cold start" generation from text prompts, their needs in data and computing resources, as well as the ambiguities around intellectual property and privacy concerns pose certain counterarguments for their utility. An interesting and relatively unexplored alternative has been the introduction of unconditional synthesis from a single sample, which has led to interesting generative applications. In this paper we focus on single-shot motion generation and more specifically on accelerating the training time of a Generative Adversarial Network (GAN). In particular, we tackle the challenge of GAN's equilibrium collapse when using mini-batch training by carefully annealing the weights of the loss functions that prevent mode collapse. Additionally, we perform statistical analysis in the generator and discriminator models to identify correlations between training stages and enable transfer learning. Our improved GAN achieves competitive quality and diversity on the Mixamo benchmark when compared to the original GAN architecture and a single-shot diffusion model, while being up to x6.8 faster in training time from the former and x1.75 from the latter. Finally, we demonstrate the ability of our improved GAN to mix and compose motion with a single forward pass.

本文研究了从单个样本的无条件合成中快速生成动作的训练时间并通过精心调节损失函数权重以防止模式崩溃，通过在生成器和鉴别器模型中进行统计分析来实现转移学习，并在 Mixamo 基准测试中展示比原始 GAN 架构和单次扩散模型更高的质量和多样性，训练时间更快。最后，我们展示了改进的 GAN 在单次前向传递中混合和组合运动的能力。

实用的单帧动作合成