Added new example paper: examples/generation5/ paper on temporal motion discriminator

Author: Shivamkak19

templates/amp/examples/generation5/graph.png

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+[
+    {
+        "Name": "adaptive_reward_weighting",
+        "Title": "Dynamic Loss Balancing in AMP: Adaptive Reward Weighting for Improved Motion Imitation",
+        "Experiment": "Implement an adaptive reward weighting system that dynamically adjusts the balance between task and style rewards based on their relative magnitudes during training. The weights should be updated using a moving average of loss ratios. Compare pose error and task performance against the baseline AMP implementation.",
+        "Interestingness": 7,
+        "Feasibility": 8,
+        "Novelty": 6,
+        "novel": false
+    },
+    {
+        "Name": "hierarchical_discriminator",
+        "Title": "Multi-Scale Motion Assessment: Hierarchical Discriminators for Natural Movement Generation",
+        "Experiment": "Modify the AMP discriminator to use a hierarchical architecture with both local and global motion discriminators. The local discriminator focuses on frame-level features while the global discriminator assesses longer temporal sequences. Compare the quality of generated motions against the baseline single discriminator approach.",
+        "Interestingness": 8,
+        "Feasibility": 7,
+        "Novelty": 7,
+        "novel": false
+    },
+    {
+        "Name": "dataset_specialization",
+        "Title": "Targeted Motion Priors: Investigating Dataset Specialization in AMP",
+        "Experiment": "Compare the performance of AMP when trained on specialized motion datasets (e.g., HumanEva focused on basic locomotion) versus general motion capture collections (e.g., full AMASS dataset). Evaluate pose error and motion naturalness for specific tasks like walking.",
+        "Interestingness": 6,
+        "Feasibility": 9,
+        "Novelty": 5,
+        "novel": false
+    }
+]

templates/amp/examples/generation5/ideas.json

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+Processing report5.pdf...
+[========================================] (8/8)
+4
+Reject
+['Insufficient clarity in the description of the methodology, particularly the autoencoder aggregator and adaptive momentum-based smoothing.', 'Lack of theoretical justification for the proposed techniques.', 'Insufficient empirical evidence to robustly support the claimed improvements.', 'Limited experimental validation on a few motion types and lack of comparison with more advanced state-of-the-art methods.', 'The paper does not adequately address potential limitations and ethical concerns.']