Paper: HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance (ICLR-2024)

Reviewer comment (Evaluation):
The experimental results are all qualitative results. It is good to have a metric/metrics to compare quantitatively against prior methods; For example, to measure the CLIP similarity between the text prompts and the generated contents; Otherwise, it is difficult to evaluate the performance since we can deliberately select good performing prompts over prior methods for comparisons.

Author response:
**Q: “It is good to have a metric/metrics to compare quantitatively against prior methods; For example, to measure the CLIP similarity”**

Please refer to the answer to the above question.

Reviewer comment (Experiments):
For the kernel smoothing, you only choose [1, 1, 1] as the sliding window kernel, have you tried other choices?

Author response:
**Q:“For the kernel smoothing, you only choose [1, 1, 1] as the sliding window kernel, have you tried other choices?”**

In addition to using the proposed kernel [1, 1, 1], we also experimented with alternative weighted kernels. For example, we set the kernel K = scale_k * [1, 6, 1], where scale_k represents a scale parameter, and [1, 6, 1] can be interpreted as the importance weights assigned to the current signal and its neighbors within the kernel window. We also varied the weights [1, 6, 1] to explore different proportions. Our experiments showed no significant differences. Notably, as the PDF estimated from the coarse stage has broad coverage in non-zero density regions, and the number of sampling points is fair (e.g., 36 points per ray, compared to 64-128 points used in prior works) during the refined stage, the rendered output does not contain flickering.

Reviewer comment (Writing):
How is the kernel smoothing conducted for coarse-to-fine importance sampling? Could the authors provide more details? In fact, I do not understand "kernel smoothing". Use an equation to explain it would be very helpful. Figure 3 seems only presents the results with/without kernel smoothing.

Author response:
**Q: “How is the kernel smoothing conducted for coarse-to-fine importance sampling? Could the authors provide more details?”**

**Motivation:**

Recall that NeRFs usually use a hierarchical sampling procedure with two distinct MLPs, one “coarse” and one “fine.” The coarse MLP captures the broad information of the scene, and the fine MLP captures detailed features. This is necessary for NeRF because the MLPs were only able to learn a model of the scene for one single scale (either the "coarse” or the "fine” scale) [1]. However, using two MLPs can be expensive. 

*Thus, how to use a single MLP that can learn multiscale representations of the scene?* To address this, we propose a kernel smoothing (KS) approach. The KS approach involves flattening the estimated density in the coarse stage, enabling it to capture a broad signal range of the scene.

**Details:**

Specifically, the KS approach is a weighted moving average of neighboring signals. The weight is defined by a kernel. In practice, for each estimated weight point v_i in the coarse stage, we choose the kernel window as N and compute a weighted average for all signals within the kernel window:

>v_i = \frac{\sum_{j=1}^{N} K_j \cdot v_{i+j- \lfloor \frac{N}{2} \rfloor}}{\sum_{j=1}^{N} K_j}

In practice, we set K = [1,1,1].

**Paper modification and code release**:

We have included a detailed explanation of the KS approach in Sec.4.2 of the manuscript. Additionally, we will provide code implementation with the final version.

[1] Jon Barron et al., Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields, ICCV 2021.

Reviewer comment (Presentation):
The contributions presented in the paper seem fragmented, lacking a cohesive thread or central theme. It would enhance the paper's clarity and impact if the authors could refine the structure.

Author response:
**Q: “The contributions presented in the paper seem fragmented, lacking a cohesive thread or central theme. It would enhance the paper's clarity and impact if the authors could refine the structure.”**

We thank the reviewer for the useful feedback. We believe that the innovative idea of the paper is well-organized when viewed at a systemic level, aiming to enhance the entire text-to-3D generation system by refining its two essential components: **(1) representation** and **(2) supervision**. These improvements result from a synergistic fusion of various methods. As a result, our contributions stand as comprehensive improvements across every component of the system, incorporating nuanced ``look-like” discrete details.

Specifically, within the framework of text-to-3D generation, we  enhance generation quality for representation and supervision:
- For representation, we introduce effective regularizations and an advanced importance sampling approach in NeRFs.  
- For supervision, we propose a novel training schedule and an advanced loss function for score distillation.

We reorganized our manuscript to enhance its structure and emphasize the contributions for the entire text-to-3D framework.

Reviewer comment (Novelty):
The contributions of the paper are discrete, which comprises several small contribution points.

Author response:
We thank the reviewer for the useful feedback. We believe that the innovative idea of the paper is well-organized when viewed at a systemic level,: aiming to enhance the entire text-to-3D generation system by refining its two essential components: **(1) 3D representation** and **(2) optimization**. These improvements result from a synergistic fusion of various methods. As a result, our contributions stand as comprehensive improvements across every component of the system, incorporating nuanced ``look-like” discrete details under a solid base insight.

Specifically, within the framework of text-to-3D generation, we  enhance generation quality for both representation and optimization:
- For 3D representation, we introduce effective regularizations and an advanced importance sampling approach in NeRFs.  
- For optimization, we propose a novel training schedule and an advanced loss function for score distillation.

We reorganized our manuscript to enhance its structure and emphasize the contributions for the entire text-to-3D framework.

Reviewer comment (Experiments):
Maybe a user-study should be conducted to quantitatively evaluate the method.

Author response:
For a more comprehensive evaluation, both qualitative and quantitative, we obtained 30 text prompts from DreamFusion and conducted experiments using our method and Fantasia3D. We followed the training instructions from the official repository of Fantasia3D.

**Qualitative comparison**: Additional visual comparisons to Fantasia3D were included in Fig.14-15 (10 samples) of the Appendix. We also added a comparison of the rendered videos (``fantasia3d.mp4”, 30 samples) in the supplementary material. Notably, we observed a low success rate in the geometry generation stage using Fantasia3D, even with a higher number of training iterations. We attribute this to the increased difficulty of learning DMTet with the SDS loss, especially when compared to other implicit representations such as NeRFs.

**Quantitative comparison**: We added a user study and computed the CLIP similarity for evaluation:

**(1) User study**: We conduct a user study for our method and Fantasia3D. In the survey, we present rendered 2D images of the 30 generated 3D objects. Users are asked to choose the result that, in their opinion, exhibits overall better quality. Below, we report the macro-average rate of preference for each method across the 30 objects. The results indicate that our method achieves a higher preference than Fantasia3D in terms of overall quality.

| Method | Preference | 
|----------|----------|
| Fantasia3D | 9.7% |
| Ours | 90.3% | 


**(2) CLIP similarity**: We also compare our method to Fantasia3D using CLIP-Similarity(↑). In this evaluation, we render 100 images for each generated object and compute the averaged CLIP similarity for the text-image pairs. We use the model "openai/clip-vit-base-patch16” for this evaluation. The scores show that our method achieves slightly better clip similarity than Fantasia3D.

| Method | CLIP-Similarity (↑)| 
|----------|----------|
| Fantasia3D | 0.302 |
| Ours | 0.344 | 


We have incorporated these evaluations into the manuscript, highlighted in the red color.

Reviewer comment (Experiments):
Basically the proposed strategies can be generalized to other baseline methods, for example, ProlificDreamer [1]. I’m curious to see the performance with other baseline methods.

Author response:
We have integrated the z-variance loss into ProlificDreamer, and the results are presented in Fig.19 in the appendix. Our observations indicate that adding the z-variance loss leads to sharper textures, highlighting the effectiveness and generalizability of the approach.

Additionally, our proposed latent- and image-space SDS loss has been incorporated in various image-to-3D [1] works, particularly in cases where image-space supervision is necessary. Our SDS loss has proven effective in addressing over-saturation issues in these contexts.

[1] Huang et al., HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation, 2023.

We also incorporate our proposed method into ProlificDreamer including the z-variance loss, the image-space loss and the square root timestep annealing schedule. We used the unofficial implementation of ProlificDreamer (https://github.com/threestudio-project/threestudio) due to the absence of official code release. The results are shown in Fig.20 in the appendix.pdf. We observe that that the incorporation of our method into ProlificDreamer significantly improves generation quality, enhancing both detailed textures and sharper shapes.