Unbounded: A Generative Infinite Game of Character Life Simulation
Authors: Jialu Li, Yuanzhen Li, Neal Wadhwa, Yael Pritch, David E. Jacobs, Michael Rubinstein, Mohit Bansal, Nataniel Ruiz
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our system through both qualitative and quantitative analysis, showing significant improvements in character life simulation, user instruction following, narrative coherence, and visual consistency for both characters and the environments compared to traditional related approaches. |
| Researcher Affiliation | Collaboration | 1Google 2The University of North Carolina at Chapel Hill |
| Pseudocode | No | The paper describes methods and architectures but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "https://infinite-generative-game.github.io/" which is a project website. A visit to the website indicates "Code coming soon!", thus the code is not currently available. |
| Open Datasets | No | We collect an evaluation dataset consisting of 5,000 (character image, environment description, text prompt) triplets with GPT4o (Open AI, 2023). |
| Dataset Splits | Yes | We collect an evaluation dataset consisting of 5,000 (character image, environment description, text prompt) triplets with GPT4o (Open AI, 2023). It includes 5 characters (dog, cat, panda, witch, and wizard), 100 diverse environments, and 1,000 text prompts (10 per environment). ... We collect an additional evaluation dataset with 100 user-simulator interaction samples ... We distill the LLM using 5,000 user-simulator interaction samples collected from GPT-4o. |
| Hardware Specification | Yes | We train a Dream Booth Lo RA of rank 16 with batch size 1 and a constant learning rate 1e-4 for 500 steps on a single A100... We train the LLM for 6,500 steps, with batch size 8, distributed across 4 A100s, and learning rate 1e-4. |
| Software Dependencies | No | The paper mentions using models like SDXL and Gemma-2B as foundations but does not specify versions for other ancillary software dependencies like programming languages or libraries (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | We train a Dream Booth Lo RA of rank 16 with batch size 1 and a constant learning rate 1e-4 for 500 steps... The dynamic mask ratio r% in set to be 60%. ... We train the LLM for 6,500 steps, with batch size 8... and learning rate 1e-4. The learning rate scheduler is set to be cosine annealing (Loshchilov & Hutter, 2016), and the warmup steps ratio is 0.03. |