Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper presents the first 3D morphable modelling approach, where a 3D face shape can be defined using a textual prompt. This work extends the FLAME model to a common image-text space. This allows for direct shape manipulation from textual descriptions. Such work has many generative applications.
strengths_and_weaknesses: Strengths
- Clearly defined research problem, motivation and background.
- The approach has been clearly explained.
- Discussion of future work on responsible AI considerations around face generation are absolutely crucial and it is great that the authors have recognized this.

Weakness
- The background could include more relevant citations. For example, [1] for text-to-image generation

[1] Yu, Jiahui, et al. "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation." Transactions on Machine Learning Research.
suggested_changes: Add relevant prior work to the discussion.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper connects the space of CLIP image-caption embeddings to the parameter space of a 3DMM (FLAME + DECA), by training a deep MLP on a dataset of StyleGAN-2 generated faces and inferred (CLIP, FLAME+DECA) embedding pairs. This allows a 3D face to be initialized from a text description of the face.
strengths_and_weaknesses: Strengths
* the paper is clearly written, well referenced, with nice figures
* the motivation for the paper is clear and the approach brings together and makes good use of a lot of recent models - CLIP, FLAME, DECA, StyleGAN2, DALL-E
* although not directly reproducible, the work is clearly explained enough that one would be able to set up a similar experiment
* the qualitative results presented show good promise across a variety of text prompts

Weaknesses
* the claim that "the method shows a better (than average) shape initialisation" should ideally be backed up quantitatively
* the detail about how "using texture mapping to map this to the mesh" could be provided -- it's a bit unclear in the paper and otherwise rather surprising that the DALL-E generated images have a similar pose

suggested_changes: Besides the clarification above and the few minor points below, I think this is an interesting paper which people will enjoy reading and thinking about in the workshop.

Minor 
* Fig 1 - order the legend by order of appearance from left to right to make easier to scan. Or color-code things. 
* Use `` for opening quotation marks
* I think it would be nice to show more examples of just the 3D generation (since this is the key contribution of the paper) in the main text, e.g. a grid in which certain specific attributes are varied. This might help to assess the ability of the model to support the generation of police photofits - as in that application, the witness might often say e.g. "the chin is more pointy", or "the cheekbones are more prominent" - i.e. exploring smaller variations of the 3DMM space with targeted textual feedback. This can tie the contribution of the model back to one of the original motivations, and show whether there is still more work to be done (which can be a good thing - it means there are more things to be discovered!), or the model might be already very useful.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper proposes a method to generate 3D deformable faces equipped with texture, starting solely from a text description. The method is based on a combination of CLIP, FLAME, DECA, and DALL-E. Several qualitative results are reported.
strengths_and_weaknesses: STRENGTHS
======
1) Unifying text and 3D Morphable Models is a hot topic at the moment, and such approaches have a tremendous potential impact in the future. 
2) The reported qualitative examples provide some nice stress with both for the poses and identities of the considered subjects. This indeed suggests method flexibility

WEAKNESSES
========
1) The paper provides a nice discussion of previous work, but it would benefit from a better positioning, especially w.r.t. Aneja et al. 2022. This latter architecture highly resembles the one proposed in this paper, and I have a hard time understanding the main contribution from that. The paper suggests that "none of these methods considers the generation of a fully parametrised model of the human face, including identity", but Aneja et al. 2022 are significantly close. The main difference that I see is that their input texts seem quite limited, but given that they also rely on DECA, FLAME and CLIP I wonder what is actually the real methodological difference.
2) I see the paper obtains textured 3DMM, but no experiments are performed to show the deformation capability of the obtained model. This is a critical and important point to address because the results look quite interesting, and they may be even more exciting if some manipulations are provided.
3) Evaluating the generation capability of a network quantitatively is always challenging, but I think some numerical results are needed to validate the method's performance. Following the protocol of Aneja et al., I think that FID and KID might be good metrics which would provide convincing evaluations to the reader.
suggested_changes: To address the above weaknesses, I suggest the following changes:
1) Incorporate a discussion of Aneja et al. 2022, especially highlighting novelty and contribution w.r.t. to them. If it is possible, provide comparisons. Given that now the code is not available yet, it is also possible to test some of their qualitative prompts and compare the quality with the one provided by this method. 
2) Add manipulation experiments, showing the capability of the obtained 3DMM and the consistency across different shapes and poses. This would strengthen the potential applications of the paper and point the actual advantage of learning a 3DMM.
3) Add quantitative analysis, with comparison w.r.t. a baseline. The most naive baseline could be a nearest-neighbour one (e.g., given a new text, we pick the nearest texture and mesh from the training dataset to show the generalization of the method), but of course, having something more sophisticated would be better.
4) I would also suggest having a more focused title since "multi-modal" sounds generic. To give a suggestions: "Text2Face: 3D Textured Morphable Faces from Text Input"
review_title: null

Decision: Invite to archive