Update README.md

README.md

@@ -7,7 +7,7 @@ Code for CVPR 2022 (oral) paper "Bailando: 3D dance generation via Actor-Critic
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-	<img src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif1.gif" width="170" /> <img src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif2.gif" width="380" /> <img width="300" src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif3.gif"/>
+	<img src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif1.gif" width="150" /> <img src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif2.gif" width="360" /> <img width="280" src="https://github.com/lisiyao21/Bailando/blob/main/gifs/dance_gif3.gif"/>
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 > Driving 3D characters to dance following a piece of music is highly challenging due to the **spatial** constraints applied to poses by choreography norms. In addition, the generated dance sequence also needs to maintain **temporal** coherency with different music genres. To tackle these challenges, we propose a novel music-to-dance framework, **Bailando**, with two powerful components: **1)** a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequence to a quantized codebook, **2)** an actor-critic Generative Pre-trained Transformer (GPT) that composes these units to a fluent dance coherent to the music. With the learned choreographic memory, dance generation is