Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
Authors: Junzhe Zhu, Peiye Zhuang, Sanmi Koyejo
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our method over previous approaches, enabling the generation of highly detailed and view-consistent 3D assets through a single-stage training process. |
| Researcher Affiliation | Collaboration | Junzhe Zhu 1, Peiye Zhuang 1,2, Sanmi Koyejo1 1Stanford University, 2Snap Inc. |
| Pseudocode | Yes | Algorithm 1 Training Procedure |
| Open Source Code | No | Our approach is implemented based on a publicly available repository 2. (Footnote 2: https://github.com/ashawkey/stable-dreamfusion/tree/main) The paper states their approach is *based on* a public repository, but does not explicitly state that *their modified code* (for the work described in this paper) is released or provided with a link. |
| Open Datasets | No | The paper refers to using pre-trained models (e.g., Stable Diffusion (Rombach et al., 2022)) but does not specify a publicly available dataset that *their* method is trained on or fine-tuned with in the traditional sense of a dataset for explicit training splits. Their method optimizes a 3D representation using guidance from these pre-trained models based on text prompts. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes optimization procedures rather than traditional dataset-based training. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models) used for running its experiments. It mentions an 'instant-ngp' for positional encoding but no hardware details. |
| Software Dependencies | No | The paper mentions several software components like 'Adam', 'DDIM', 'SD model', 'Deep Floyd IF model', 'T5-XXL', and 'instant-ngp', along with their respective citations, but does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | Training setup. We use Adam (Kingma & Ba, 2015) with a learning rate of 10 2 for instantngp encoding, and 10 3 for Ne RF weights. In practice, we choose total iter as 104 iterations. The rendering resolution is 512 512. We employ DDIM (Song et al., 2021) with empirically chosen parameters r = 0.25, and η = 1 to accelerate training. We choose the hyper-parameters λrgb = 0.1, λd = 0.1, and λzvar = 3. Similar to prior work (Poole et al., 2022; Lin et al., 2023; Wang et al., 2023a), we use classifier-free guidance (Ho & Salimans, 2022) of 100 for our diffusion model. |