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@@ -625,7 +625,7 @@ <h3>Framework Overview</h3>
           In (b), human motion is fed into the human motion encoder to produce a semantic-aware, subject-invariant latent code <em>Z</em>. 
           The CLIP feature of the subject-translated sentence and <em>Z</em> are concatenated together and passed into the animal decoders.
           During inference in (c), we generate animal motions based on human motion sequences sampled from generative models.
-          Details on the architecture, loss functions, and inference process are elaborated in paper.
+          Details on the architecture, loss functions, and inference process are elaborated in the paper.
         </p>
     </div>
 
@@ -636,7 +636,8 @@ <h2>Bibtex</h2>
         <p class="description">
 @misc{yang2023omnimotiongpt,
       title={OmniMotionGPT: Animal Motion Generation with Limited Data}, 
-      author={Zhangsihao Yang and Mingyuan Zhou and Mengyi Shan and Bingbing Wen and Ziwei Xuan and Mitch Hill and Junjie Bai and Guo-Jun Qi and Yalin Wang},
+      author={Zhangsihao Yang and Mingyuan Zhou and Mengyi Shan and Bingbing Wen and Ziwei Xuan and Mitch Hill  <br>
+		and Junjie Bai and Guo-Jun Qi and Yalin Wang},
       year={2023},
       eprint={2311.18303},
       archivePrefix={arXiv},