Computer generated animations are becoming more realistic every day. This trailer can best be seen in video games. think first Lara Croft in it tomb Raider series and the most recent Lara Croft. We went from a 230 polygon puppet making funny moves to a real character moving smoothly on our screens.
Generating natural and diverse motion in computer animation has long been a challenging problem. Traditional methods such as motion capture systems and manually creating animations are known to be costly and time consuming, resulting in limited motion data sets that lack diversity in style, skeletal structures, and types. of models. This manual and slow nature of animation generation brings a need for an automated solution in the industry.
Existing data-based motion synthesis methods have limited effectiveness. However, in recent years, deep learning has become a powerful technique in computer animation, capable of synthesizing diverse and realistic movements when trained on large and comprehensive data sets.
Deep learning methods have shown impressive results in motion synthesis, but have drawbacks that limit their practical applicability. First, they require long training times, which can be a major bottleneck in the animation production process. Second, they are prone to visual artifacts, such as juddering or excessive smoothing, which affect the quality of synthesized movements. Lastly, they have a hard time scaling well to large, complex skeletal structures, limiting their use in scenarios where intricate movement is required.
We know that there is a demand for a reliable motion synthesis method that can be applied in practical scenarios. However, these problems are not easy to overcome. So what can be the solution? time to meet with GenMM.
GenMM is an alternative approach based on the classical idea of nearest neighbor motion and motion coincidence. It uses motion matching, a widely used industry technique for character animation, and produces high-quality animations that look natural and adapt to different local contexts.
GenMM is a generative model that can extract various moves from a single or a few example sequences. It does this by leveraging an extensive motion capture database as an approximation of all natural motion space.
GenMM incorporates bidirectional similarity as a new cost generating function. This similarity measure ensures that the synthesized motion sequence contains only motion patches from the provided examples and vice versa. This approach maintains motion matching quality while allowing for generative capabilities. To further enhance diversity, it uses a multi-stage framework that progressively synthesizes motion sequences with minimal distribution discrepancies compared to the examples. Furthermore, an unconditional noise input is introduced into the pipeline, inspired by the success of GAN-based methods in image synthesis, to achieve highly diverse synthesis results.
In addition to its ability for diverse motion generation, GenMM also proves to be a versatile framework that can be extended to various scenarios beyond motion matching capabilities alone. These include motion completion, keyframe-guided generation, infinite looping, and motion reassembly, demonstrating the wide range of applications enabled by the generative motion matching approach.
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Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She received her Ph.D. He graduated in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning.” His research interests include deep learning, computer vision, video encoding, and multimedia networking.