Pose Priors from Language Models

## Abstract

Abstract We present a zero-shot pose optimization method that enforces accurate physical contact constraints when estimating the 3D pose of humans.
Our central insight is that since language is often used to describe physical interaction, large pretrained text-based models can act as priors on pose estimation. We can thus leverage this insight to improve pose estimation by converting natural language descriptors, generated by a large multimodal model (LMM), into tractable losses to constrain the 3D pose optimization.
Despite its simplicity, our method produces surprisingly compelling pose reconstructions of people in close contact,
correctly capturing the semantics of the social and physical interactions. We demonstrate that our method rivals more complex state-of-the-art approaches that require expensive human annotation of contact points and training specialized models. Moreover, unlike previous approaches, our method provides a unified framework for resolving self-contact and person-to-person contact.

## 1 Introduction

Which social signals should we model when building models of human behavior? This paper focuses on touch, one of the strongest signals of social validation and rejection.
As physical contact is a universal human social signal, humans developed extensive terminology for its particularities.
Detailed descriptions of touch in different contexts are widely discussed in texts that range from love-song lyrics such as Paul Anka’s “Put your head on my shoulder” to Shakespeare’s “See how she leans her cheek upon her hand.” (Romeo and Juliet). It touches on subjects from love to meditative poses.

Our goal in this work is to enable computers to correctly perceive physical contact (See Figure 1). Specifically, we aim to build a system that takes as input a single view of people during close physical interaction or one person in a pose that involves self-contact and produces accurate 3D mesh reconstructions of each person as output. This setting is challenging for state-of-the-art pose regression models, as some body parts are frequently occluded by other ones, and also challenging for pose optimization methods relying on 2D keypoints, which do not convey contact points. Previously proposed approaches address these issues by curating task-specific datasets via motion capture or human-annotated points of contact between body parts [, , ].

Our main insight is that since written language discusses our physical interactions (hugs, kisses, fist fights, yoga poses, etc.) at great length, we should be able to extract a semantic prior on humans’ poses
from a pretrained large multimodal model (LMM) [, , ]. Just like a prior trained on motion capture data, this language-based prior can tell us which contacts are most likely in poses and interactions.
Through this approach, we avoid the time-consuming and expensive collection of training data involving motion capture or annotated self and cross-person contacts that previous refinement methods require.

This insight leads us to a simple zero-shot framework,
which we call ProsePose.(^1^11Project page: [prosepose.github.io](https://prosepose.github.io)) We prompt a pre-trained LMM, with the image and request as output a formatted list of contact constraints between body parts. We then convert this list of constraints into a loss function that can be optimized jointly with other common losses, such as 2D keypoint loss, to refine the initial estimates of a pose regression model. The prompt provides an intuitive way for the system designer to adapt the generated constraints to their setting (e.g. if they want to focus on yoga or dance).

We show in experiments on three 2-person interaction datasets and one dataset of complex yoga poses that ProsePose produces more accurate reconstructions than previous zero-shot approaches. These results indicate that LMMs, without any additional finetuning, offer a useful prior for pose reconstruction.

In summary, (1)
we show that LMMs have implicit semantic knowledge of poses that is useful for pose estimation, and (2) we formulate a novel zero-shot framework that converts free-form natural language responses from the LMM into tractable loss functions that can be used for pose optimization.

## 2 Related Work

### 2.1 3D human pose reconstruction

Reconstructing 3D human poses from single images is an active area of research. Prior works have explored using optimization-based approaches [, , , , ] or pure regression [, , , , ] to estimate the 3D body pose given a single image. HMR2 [] is a recent state-of-the-art regression model in this line of work.
Building on these monocular reconstruction approaches, some methods have looked into reconstructing multiple individuals jointly from a single image. These methods [, , ] use deep networks to reason about multiple people in a scene to directly output multi-person 3D pose predictions.
BEV [] accounts for the relative proximity of people explicitly using relative depth annotations to reason about proxemics when predicting and placing each individuals in the scene (e.g. depth of people with respect to one another). However, approaches in both categories generally do not accurately capture physical contact between parts of a single person or between people [, ].

### 2.2 Contact inference in 3D pose reconstruction

3D pose reconstruction is especially challenging when there is self-contact or inter-person contact. This has motivated a line of work on pose reconstruction approaches tailored for this setting.
[] focuses on predicting self contact regions for 3D pose estimation by leveraging a dataset with collected contact annotations to model complex poses such as arm on hip or crossed arms.
[] introduces the first dataset with hand-annotated ground truth contact labels between two people. REMIPS [] and BUDDI [] train models on the person-to-person contact maps in this data in order to
improve 3D pose estimation of multiple people from a single image.
However, contact annotations, which are crucial for these approaches, are difficult and expensive to acquire. Our method does not require any training on such annotations.
Instead, we leverage an LMM’s implicit knowledge about pose to constrain pose optimization to capture both self- and person-to-person contact.

### 2.3 Language priors on human pose

There exists a plethora of text to 3D human pose and motion datasets [, , ], which have enabled work focused on generating 3D motion sequences of a single person performing a general action [, , ]. This line of work has been extended to generating the motion of two people conditioned on text [, ].

PoseScript [] is
a method for generating a single person’s pose from fine-grained descriptions. They leverage a library of predefined pose descriptors, which they stitch together to form detailed textual annotations for each motion capture pose
in their dataset. By training a model on these language annotations and associated poses, they can generate various plausible poses.

PoseFix [] considers the problem of modifying a pose given a fine-grained description of the desired change, and introduces a dataset of triplets, each consisting of an initial pose, a different but related pose, and the description of the change. The PoseFix method then trains a model on this data to predict the modified pose given the initial pose and description.

PoseGPT [], like our work, focuses on the problem of monocular 3D reconstruction of people. PoseGPT is a pose regressor that uses language as part of its training data. However, PoseGPT does not produce better pose estimates than previous state-of-the-art regressors (i.e. regressors that do not use language) and applies only to the one-person setting.

Our work differs from previous work on language and pose in several ways. First, whereas all prior work trains a model on data with pairs of language and pose, which is expensive to collect, our method leverages the existing knowledge in an LMM to reason about pose.
Second, prior work on language and pose focuses on either the one-person or the two-person setting. In contrast, our work unifies both in a single framework which we use to reason about physical contacts within or between poses. Finally, in scenes with physical contact, we show that our language-based method improves the pose estimates of state-of-the-art regressors.

Figure: Figure 2: LMM-guided Pose Estimation Our method takes as input an image of one or two people in contact. We first obtain initial pose estimates for each person from a pose regressor. Then we use an LMM to generate contact constraints, each of which is a pair of body parts that should be touching. This list of contacts is converted into a loss function $\mathcal{L}_{\text{LMM}}$. We optimize the pose estimates using $\mathcal{L}_{\text{LMM}}$ and other losses to produce a refined estimate of each person’s pose that respects the predicted contacts.

## 3 Guiding Pose Optimization with an LMM

Given an image, our goal is to estimate the 3D body pose of individuals in the image while capturing the self and cross-person contact points.
While we cannot trivially use natural language responses (hug, kiss) to directly optimize 3D body poses, we leverage the key insight that LMMs understand *how* to articulate a given pose (arms around waist, lips touching). We propose a method to structure these articulations into constraints and convert them into loss functions.

More concretely, our framework, illustrated by Figure 2, takes as input the image $\bm{I}$ and the bounding boxes $\bm{B}$ of the subjects of interest. In the first stage, the image is passed to a pose regressor to obtain a rough estimate of the 3D pose $\bm{X}^{p}$ for each individual $p$ in the image. In the second stage, we prompt a LMM with the image and a set of instructions in order to generate a list of self- or inter-person contact constraints, which we then convert into a loss function (Sec. 3.4). Finally, in the third stage, we jointly optimize the generated loss function with several other pre-defined loss terms (Sec. 3.4). We refer to our framework as ProsePose .

### 3.1 Preliminaries

While our approach scales in principle to an arbitrary number of individuals, we focus our description on the two-person case to keep the exposition simple. We also demonstrate results on the one-person case, which is simply an extension of the two-person case. In particular, we apply our method to the one-person case by setting $X^{0}=X^{1}$. Please see § 7
for details on the differences between the two-person and one-person cases.

Large Multimodal Models
An LMM is a model that takes as input an image and a text prompt and produces text output that answers the prompt based on the image. Our framework is agnostic to the architecture of the LMM. LMMs are typically trained to respond to wide variety of instructions [, ], but at the same time, LMMs are prone to hallucination [, ]. Handling cases of hallucination is a key challenge when using LMMs, and we mitigate this issue by aggregating information across several samples from the LMM.

Pose representation.
We use a human body model [] to represent each person $p\in\{0,1\}$. The body model is composed of a pose parameter that defines the joint rotations $\bm{\theta}\in\mathbb{R}^{d_{\theta}\times 3}$, where $d_{\theta}$ is the number of joints, and a shape parameter $\bm{\beta}\in\mathbb{R}^{d_{\beta}}$, where $d_{\beta}$ is the dimensions of the shape parameter. We can apply a global rotation $\bm{\Phi}\in\mathbb{R}^{3}$ and translation $\bm{t}\in\mathbb{R}^{3}$ to place each person in the world coordinate space.
The full set of parameters for each person is denoted by $\bm{X}^{p}=[\bm{\theta}^{p},\bm{\beta}^{p},\bm{\Phi}^{p},\bm{t}^{p}]$.
For simplicity, we refer to the parameter set $(\bm{X}^{0},\bm{X}^{1})$ as $\bm{X}$.

These parameters can be plugged into a differentiable function that maps to a mesh consisting of $d_{v}$ vertices $\bm{V}\in\mathbb{R}^{d_{v}\times 3}$. From the mesh, we can obtain a subset of the vertices representing the 3D locations of the body’s joints $\bm{J}\in\mathbb{R}^{d_{j}\times 3}$.
From these joints, we can calculate the 2D keypoints $\bm{K}_{proj}$ by projecting the 3D joints to 2D using the camera intrinsics $\Pi$ predicted from [].

$$ $\bm{K}_{proj}=\Pi\left(\bm{J}\right)\in\mathbb{R}^{d_{j}\times 2}.\vspace{-0.5cm}$ (1) $$

Vertex regions.

Figure: Figure 3: Notation. Given an image $\bm{I}$, we can lift each individual into corresponding 3D meshes $\bm{V}$. We define contact constraints $\bm{c}$ as pairs of regions $(\bm{R}_{a},\bm{R}_{b}$) in contact. The loss is defined in terms of the distance between the vertices $(\bm{v}_{a},\bm{v}_{b}$) on the mesh.

In order to define contact constraints between body parts, we define a set of *regions* of vertices.
Prior work on contact has partitioned the body in to fine-grained regions  [].
However, since our constraints are specified by a LMM trained on natural language, the referenced body parts are often coarser in granularity.
We therefore update the set of regions to reflect this language bias by combining these fine-grain regions into larger, more commonly referenced body parts
such as arm, shoulder (front&back), back, and waist (front&back). Please see § 7.2 for a visualization of the coarse regions. Formally, we write $\bm{R}\in\mathbb{R}^{d_{r}\times 3}$ to denote a region with $d_{r}$ vertices, which is part of the full mesh ($\bm{R}\subset\bm{V}$).

Constraint definition.
A contact constraint specifies which body parts from two meshes should be touching.
Using the set of coarse regions, we define contact constraints as pairs of coarse regions $\bm{c}=(\bm{R}_{a},\bm{R}_{b})$ between a region $\bm{R}_{a}$ of one mesh and $\bm{R}_{b}$ of the other mesh, as shown in Figure 3. For instance, (“hand”, “arm”) indicates a hand should touch an arm.

### 3.2 Pose Initialization

We obtain a rough initial estimate of the 3D pose from a regression-based method. The regressor takes as input the image $\bm{I}$ and outputs estimates for the body model parameters $\bm{\theta},\bm{\beta},\bm{r}$, and $\bm{t}$ for each subject.

### 3.3 Constraint generation with a LMM

Our method strives to enforce contact constraints for the estimated 3D poses.
Our key insight is to leverage a LMM to identify regions of contact between different body parts on the human body surface.
As shown in Figure 2 (top), we prompt the LMM with an image and ask it to output a list of all plausible regions that are in contact.
However, we cannot simply use natural language descriptions to directly optimize a 3D mesh.
As such, we propose a framework to convert these constraints into a loss function.

LMM-based constraint generation.
Given the image $\bm{I}$, we first use the bounding boxes $\bm{B}$ to crop the part containing the subjects. We then use an image segmentation model to mask any extraneous individuals.
While cropping and masking the image may remove information, we find the LMMs are relatively robust to missing context, and more importantly, this allows us to indicate which individuals to focus on.
Given the segmented image, we ask the LMM to generate a set $\bm{C}=\{\bm{c}_{1},...\bm{c}_{m}\}$ of all pairs of body parts that are touching, where $m$ is the total number of constraints the LMM generates for the image.

In the prompt, we specify the full set of coarse regions to pick from. We find that LMMs fail to reliably reference the left and right limbs correctly or consistently, so we designed this set of coarse regions such that they do not disambiguate the chirality of the hands, arms, legs, feet, and shoulders. Instead, the two hands are grouped together, the two arms are grouped together, etc.
Nevertheless, if the LMM uses “left” or “right” to reference a region, despite the instruction to not do so, we directly use the part of the region with the specified chirality rather than considering both possibilities.

Motivated by the chain-of-thought technique, which has been shown to improve language model performance on reasoning tasks [], we ask the LMM to write its reasoning or describe the pose before listing the constraints. For the full prompt used in each setting, please refer to § 7.

We sample $N$ responses from the LMM, yielding $N$ sets of constraints $\{\bm{C}_{1},\bm{C}_{2},...,\bm{C}_{N}\}$.
The next step is to convert each constraint set $\bm{C}_{j}$, where $j\in\{1,2,...N\}$, into a loss term.

Loss function generation.
We first filter out contact pairs that occur fewer than $f$ times across all constraint sets, where $f$ is a hyperparameter.
Then for each contact pair $\bm{c}=(\bm{R}_{a},\bm{R}_{b})$ in $\bm{C}_{j}$, we define $dist(\bm{c})$ as the minimum distance between the two regions:

$$ $dist(\bm{c})=\min\left\lVert\bm{v}_{a}-\bm{v}_{b}\right\rVert_{2}\quad\forall \bm{v}_{a}\in\bm{R}_{a},\forall\bm{v}_{b}\in\bm{R}_{b}$ (2) $$

where $\{\bm{v}_{a},\bm{v}_{b}\}\in\mathbb{R}^{3}$.
In practice, the number of vertices in each region can be very large. To make this computation tractable, we first take a random sample of vertices from $\bm{R}_{a}$ and from $\bm{R}_{b}$ before computing distances between pairs of vertices in these samples.
Furthermore, since the ordering of the people in the LMM constraints is unknown (i.e. does $\bm{R}_{a}$ come from the mesh defined by parameter $\bm{X}^{0}$ or  $\bm{X}^{1}$), we compute the overall loss for both possibilities and take the minimum. We use $\bm{c}^{\top}=(\bm{R}_{b},\bm{R}_{a})$ to denote the flipped ordering. We then sum over all constraints in the list $\bm{C}_{j}$:

$$ $\displaystyle{dist}_{\text{sum}}(\bm{C}_{j})=\min\left(\sum_{\bm{c}\in\bm{C}_{ j}}dist(\bm{c}),\sum_{\bm{c}\in\bm{C}_{j}}dist(\bm{c}^{\top})\right)$ (3) $$

Each constraint set sampled from the LMM is likely to contain noise or hallucination. To mitigate the effect of this, we average over all $N$ losses corresponding to each constraint set to obtain the overall LMM loss. This technique is similar to self-consistency [], which is commononly used for code generation tasks. Concretely, the overall LMM loss is defined as

$$ $\displaystyle\mathcal{L}_{\text{LMM}}=\frac{1}{N}{\sum_{j=1}^{N}dist_{sum}(\bm {C}_{j})}$ (4) $$

If a constraint set $\bm{C}_{j}$ is empty (i.e. the LMM does not suggest any contact pairs), then we set $dist_{\text{sum}}(\bm{C}_{j})=0$. If there are several such constraint sets, we infer that the LMM has low confidence about the contact points (if any) in the image. To handle these cases, we set a threshold $t$ and if the number of empty constraint sets is at least as large as $t$, we gracefully backoff to the appropriate baseline optimization procedure (described in Sections 4.1 and 4.2 for each setting).

### 3.4 Constrained pose optimization

Drawing from previous optimization-based approaches [, , ], we employ
several additional losses in the optimization. We then minimize the joint loss to obtain a refined subset of the body model parameters $\bm{X}^{\prime}=[\bm{\theta}^{\prime},\bm{\beta}^{\prime}$, $\bm{t}^{\prime}]$:

$$ $\displaystyle[\bm{\theta}^{\prime},\bm{\beta}^{\prime},\bm{t}^{\prime}]= \operatorname*{arg\,min}($ $\displaystyle\lambda_{\text{LMM}}\mathcal{L}_{\text{LMM}}+\lambda_{\text{GMM}} \mathcal{L}_{\text{GMM}}$ $\displaystyle+\lambda_{\beta}\mathcal{L}_{\beta}+\lambda_{\theta}\mathcal{L}_{\theta}$ $\displaystyle+\lambda_{2D}\mathcal{L}_{2D}+\lambda_{P}\mathcal{L}_{P})$ (5) $$

Following [], we divide the optimization into two stages. In the first stage, we optimize all three parameters. In the second stage, we optimize only $\bm{\theta}$ and $\bm{t}$, keeping the shape $\bm{\beta}$ fixed.
Here, we detail all of the remaining losses used in the optimization.

Pose and shape priors. We compute a loss $\mathcal{L}_{\text{GMM}}$ based on the Gaussian Mixture pose prior of [] and a shape loss $\mathcal{L}_{\beta}=\left\lVert\bm{\beta}\right\rVert^{2}_{2}$, which penalizes extreme deviations from the body model’s mean shape.

Initial pose loss. To ensure we do not stray too far from the initialization, we penalize large deviations from the initial pose $\mathcal{L}_{\theta}=||\bm{\theta}^{\prime}-\bm{\theta}||_{2}^{2}$.

2D keypoint loss. Similar to BUDDI [], for each person in the image, we obtain pseudo ground truth 2D keypoints and their confidences from OpenPose [] and ViTPose [].
Given this pseudo ground truth, we merge all the keypoints into $\bm{K}\in\mathbb{R}^{d_{j}\times 2}$, and their corresponding confidences into $\bm{\gamma}\in\mathbb{R}^{d_{j}}$.
From the predicted $\bm{X}^{\prime}$, we can compute the 2D projection of each 3D joint location using Equation 1.
Then, the 2D keypoint loss is defined as:

$$ $\displaystyle\mathcal{L}_{2D}=\sum_{j=1}^{d_{j}}\bm{\gamma}(\bm{K}_{proj}-\bm{ K})^{2}$ (6) $$

Interpenetration loss. To prevent parts of one mesh from being in the interior of the other, we add an interpenetration loss.
Generically, given two sets of vertices $\bm{V}_{0}$ and $\bm{V}_{1}$, we use winding numbers to compute the subset of $\bm{V}_{0}$ that intersects $\bm{V}_{1}$, which we denote as $\bm{V}_{0,1}$. Similarly, $\bm{V}_{1,0}$ is the subset of $\bm{V}_{1}$ that intersects $\bm{V}_{0}$. The interpenetration loss is then defined as

$$ $\displaystyle\mathcal{L}_{P}=\sum_{x\in\bm{V}_{0,1}}\min_{v_{1}\in\bm{V}_{1}} \left\lVert x-v_{1}\right\rVert_{2}^{2}+\sum_{y\in\bm{V}_{1,0}}\min_{v_{0}\in \bm{V}_{0}}\left\lVert y-v_{0}\right\rVert_{2}^{2}$ (7) $$

Due to computational cost, this loss is computed on low-resolution versions of the two meshes (roughly 1000 vertices per mesh).

## 4 Experiments

We conduct experiments on several datasets in the two-person and one-person settings. In this section, we first provide important implementation details and a description of the quantitative metrics that we use to evaluate our method and previous approaches. We then present quantitative and qualitative results demonstrating that ProsePose refines pose estimates to capture semantically relevant contact in each setting.

Implementation details.
Following prior work on two-person pose estimation [], we use BEV [] to initialize the poses since it was trained to predict both the body pose parameters and the placement of each person in the scene. However, on the single person yoga poses, we find that the pose parameter estimates of HMR2 [] are much higher quality, so we initialize the body pose using HMR2.

We use the SMPL-X [] body model and GPT4-V [] as the LMM with temperature $=0.7$ when sampling from it.(^2^22We access GPT4-V, specifically the gpt-4-vision-preview model, via the OpenAI API: [platform.openai.com](https://platform.openai.com). We use the “high” detail setting for image input.) We report results with LLaVA as the LMM in § 8.4. We use the Segment Anything [] as the segmentation model, used to remove extraneous people in the image (we only apply this step for FlickrCI3D, since other datasets are from motion capture).
We set $N=20$ samples in all of our experiments. For all of our 2-person experiments, $f=1$, while $f=10$ in the 1-person setting. We set $t=2$ for the experiment on the CHI3D dataset and $t=N$ for all other experiments. We set $\lambda_{\text{LMM}}=1000$ in the 2-person experiments, and $\lambda_{\text{LMM}}=10000$ in the 1-person setting. In the two-person case, all other loss coefficients are taken directly from []. In the one-person case, we find that removing the GMM pose prior and doubling the weight on the initial pose loss improves optimization dramatically, likely because the complex yoga poses are out of distribution for the GMM prior. These hyperparameters and our prompts were chosen based on experiments on the validation sets. Furthermore, following [], we run both optimization stages for at most 1000 steps. We use the Adam optimizer [] with learning rate 0.01.

For other implementation details such as prompts, the list of coarse regions
in each setting, and additional differences between the 1- and 2-person cases, please refer to § 7.

Metrics. As is standard in the pose estimation literature, we report per-person Procrustes-aligned Mean Per Joint Position Error (PA-MPJPE) in millimeters to evaluate the difference between the ground-truth (or pseudo-ground-truth) pose and predicted pose. This metric finds the best alignment between the estimated and ground-truth pose before computing the joint error.
In the two-person setting, we focus on the *joint* PA-MPJPE, as this evaluation incorporates the relative translation and orientation of the two people. For the per-person PA-MPJPE, please refer to § 8.2.

We also include the percentage of correct contact points (PCC) metric introduced by [].
This metric captures the fraction of ground-truth contact pairs that are accurately predicted. For a given radius $r$, a pair is classified as “in contact" if the two regions are both within the specified radius.
We use the set of fine-grained regions defined in [] to compute PCC.
The metric is averaged over $r\in{0,5,10,15,...,95}$ mm. Please note that since these regions are defined on the SMPL-X mesh topology, we convert the regression baselines– BEV and HMR2– from the SMPL mesh topology to SMPL-X to compute this metric. Please see § 8.1for more details on the regions and on the mesh conversion.

**Table 1: Two-person Results. Joint PA-MPJPE (lower is better) and Avg. PCC (higher is better). For FlickrCI3D, PA-MPJPE is computed using the pseudo-ground-truth fits. Bold indicates best zero-shot method in each column.**
|  | Hi4D |  | FlickrCI3D |  | CHI3D |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | $\text{PA-MPJPE}_{\downarrow}$ |  | $\text{PA-MPJPE}_{\downarrow}$ | $\text{PCC}_{\uparrow}$ |  | $\text{PA-MPJPE}_{\downarrow}$ | $\text{PCC}_{\uparrow}$ |
| *Zero-shot* |  |  |  |  |  |  |  |
| BEV [] | 144 |  | 106 | 64.8 |  | 96 | 71.4 |
| Heuristic | 116 |  | 67 | 77.8 |  | 105 | 74.1 |
| ProsePose | 93 |  | 58 | 79.9 |  | 100 | 75.8 |
| *Supervised* |  |  |  |  |  |  |  |
| BUDDI [] | 89 |  | 65.9 | 81.9 |  | 68 | 78.6 |

### 4.1 Two-person Pose Refinement

Datasets
In the two-person setting, we evaluate on three datasets, and our dataset processing largely follows [].

*Hi4D* [] is a motion capture dataset of pairs of people interacting. Each sequence has a subset of frames marked as contact frames, and we take every fifth contact frame. We use the images from a single camera, resulting in roughly 247 images.

*Flickr Close Interactions 3D (FlickrCI3D)* [] is a collection of Flickr images of multiple people in close interaction. The dataset includes manual annotations of the contact maps between pairs of people. [] used these contact maps to create pseudo-ground truth 3D meshes and curated a version of the test set to exclude noisy annotations, which has roughly 1403 images.

*CHI3D* [] is a motion capture dataset of pairs of people interacting. We present results on the validation set. There are 126 different sequences, each of which has a single designated “contact frame.” Each frame is captured from 4 cameras, so there are roughly 504 images in this set.

To develop our method, we experimented on the validation sets of FlickrCI3D and Hi4D, and a sample of the training set from CHI3D.
For our experiments, we can compute the PCC on FlickrCI3D and CHI3D, which have annotated ground-truth contact maps.
Since all baselines also use BEV for initialization, we exclude images where BEV fails to detect one of the subjects in the interaction pair.

Baselines
We compare our estimated poses to the following:

- •
BEV [] Multi-person 3D pose estimation method. Uses relative depth to reason about the spatial placement of individuals in the scene. We use BEV to initialize our method and other baselines.
- •
Heuristic A contact heuristic which includes the auxiliary losses in Section 3.4 as well as a term that minimizes the minimum distance between the two meshes.
This heuristic was introduced by [], and we use their hyperparameters for this heuristic. Please note, this baseline is used as the default when the number of empty constraint sets is at least the threshold $t$.
- •
BUDDI [] A method using a learned diffusion prior, which was trained on pairs of interacting people, to constrain the optimization. We stress that BUDDI requires a large amount of annotated training data on pairs of interacting bodies, which is not used in our method.

**Table 2: Two-person PCC. Percent of correct contact points (PCC) for five different radii $r$ in mm. Bold indicates the best zero-shot score in each column. At the ground-truth contact points, our method brings the meshes closer together than the other zero-shot methods.**
|  | $\text{PCC}_{\uparrow}$ @ $r$ on FlickrCI3D |  | $\text{PCC}_{\uparrow}$ @ $r$ on CHI3D |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | 5 | 10 | 15 | 20 | 25 |  | 5 | 10 | 15 | 20 | 25 |
| *Zero-shot* |  |  |  |  |  |  |  |  |  |  |  |
| BEV [] | 3.6 | 6.3 | 10.8 | 17.1 | 28.6 |  | 5.8 | 17.4 | 32.5 | 47.3 | 61.9 |
| Heuristic | 14.6 | 33.9 | 49.3 | 60.8 | 70.3 |  | 11.1 | 28.0 | 45.3 | 55.3 | 64.4 |
| ProsePose | 15.6 | 39.9 | 57.1 | 67.9 | 75.8 |  | 13.5 | 35.2 | 52.5 | 61.3 | 68.4 |
| *Supervised* |  |  |  |  |  |  |  |  |  |  |  |
| BUDDI [] | 18.5 | 44.2 | 61.8 | 73.1 | 80.8 |  | 15.7 | 39.4 | 57.1 | 68.8 | 78.0 |

Figure: Figure 4: Two-person results We show qualitative results from ProsePose , BUDDI [], and the contact heuristic. Under each example, we show the top 3 constraints predicted by GPT4-V and the number of times each constraint was predicted across all 20 samples. Our method correctly reconstructs people in a variety of interactions, and the predicted constraints generally align with the interaction type in each example.

Quantitative Results
Table 1 provides quantitative results on the three datasets.

Across datasets, ProsePose consistently improves over the strongest zero-shot baseline, Heuristic.

On the Hi4D dataset, ProsePose reduces 85% of the gap in PA-MPJPE between Heuristic and the fully supervised BUDDI.
On the FlickrCI3D and CHI3D datasets, ProsePose narrows the gap in the average PCC between Heuristic and BUDDI by more than one-third.
(While ProsePose achieves a better PA-MPJPE than BUDDI on FlickrCI3D, for this dataset, we rely primarily on PCC since PA-MPJPE is computed on *pseudo*-ground-truth fits.)

Figure: Figure 5: More samples improve pose estimation. On the FlickrCI3D validation set, taking more samples from the LMM and averaging the resulting loss functions improves both joint PA-MPJPE (left) and average PCC (right).

On CHI3D, ProsePose outperforms Heuristic but underperforms BEV in terms of PA-MPJPE. We find that on the subset of images where we do not default to the heuristic (i.e. on images where GPT4-V predicts enough non-empty constraint sets), the PA-MPJPE for ProsePose and BEV is 86 and 87, respectively.
In other words, in the cases where our method is

**Table 3: Ablations on Hi4D. Joint PA-MPJPE (lower is better). We evaluate the impact of each loss in our optimization on the Hi4D by removing one loss at a time. For all experiments, we use the same settings. The set of cases where we default to the baseline (Heuristic) is also kept the same.**
|  | $\text{PA-MPJPE}_{\downarrow}$ |
| --- | --- |
| All Losses | 81 |
| w/o. $\mathcal{L}_{\text{LMM}}$ | 138 |
| w/o. $\mathcal{L}_{\text{GMM}}$ | 85 |
| w/o. $\mathcal{L}_{\beta}$ | 91 |
| w/o. $\mathcal{L}_{\theta}$ | 84 |
| w/o. $\mathcal{L}_{2D}$ | 130 |
| w/o. $\mathcal{L}_{P}$ | 78 |

actually used, the joint error is slightly less than that of BEV.
As a result, we can attribute the worse overall error to the poorer performance of the heuristic. Overall, our method improves over the other zero-shot methods in terms of both joint error and PCC.

Table 2 shows the PCC for each method at various radii. The results show that ProsePose brings the meshes closer together at the correct contact points. On both the FlickrCI3D and CHI3D datasets, ProsePose outperforms the other zero-shot baselines, closing much of the gap between
the fully supervised method and the best competing baseline Heuristic.

Next, we ablate important aspects of ProsePose . In
Figure 5, we show that taking several samples from the LMM and averaging the resulting loss functions improves performance, mitigating the effect of LMM hallucination. Table 3 presents an ablation of all the losses involved in our optimization on the Hi4D validation set. $\mathcal{L}_{\text{LMM}}$ and $\mathcal{L}_{2D}$ have the greatest impact, indicating that our LMM-based loss is crucial for the large improvement in joint error.

Qualitative Results
Figure 4 shows examples of reconstructions from ProsePose , Heuristic, and BUDDI.
Below each of our predictions, we list the most common constraints predicted by GPT4-V for the image. The predicted constraints correctly capture the semantics of each interaction.
For instance, it is inherent that in tango, one person’s arm should touch the other’s back. In a rugby tackle, a player’s arms are usually wrapped around the other player. Using these constraints, ProsePose correctly reconstructs a variety of interactions, such as tackling, dancing, and holding hands. In contrast, the heuristic struggles to accurately position individuals and/or predict limb placements, often resulting in awkward distances.

### 4.2 One-person pose refinement

Datasets
Next, we evaluate ProsePose on a single-person setting.
For this setting, we evaluate on MOYO [], a motion capture dataset with videos of a single person performing various yoga poses.
The dataset provides views from multiple different cameras. We pick a single camera that shows the side view for evaluation.
For each video, we take single frame from the middle as it generally shows the main pose. There is no official test set, and the official validation set consists of only 16 poses. Therefore, we created our own split by picking 79 arbitrary examples from the training set to form our validation set. We then combine the remaining examples in the training set with the official validation set to form our test set. In total, our test set is composed of 76 examples. Since this dataset does not have annotated region contact pairs, we compute the pesudo-ground-truth contact maps using the Euclidean and geodesic distance following [].

**Table 4: One-person Results. PA-MPJPE (lower is better) and Avg. PCC (higher is better). Our method captures ground-truth contacts better than the baseline methods, as shown by the PCC.**
|  |  |  |  |  | $\text{PCC}_{\uparrow}$ @ $r$ |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | $\text{PA-MPJPE}_{\downarrow}$ |  | $\text{PCC}_{\uparrow}$ |  | 5 | 10 | 15 | 20 | 25 |
| HMR2 [] | 84 |  | 83.0 |  | 34.2 | 55.2 | 69.5 | 78.4 | 83.9 |
| HMR2+opt | 81 |  | 85.2 |  | 47.7 | 65.5 | 74.6 | 80.9 | 86.2 |
| ProsePose | 82 |  | 87.8 |  | 54.2 | 73.8 | 81.4 | 86.5 | 91.3 |

Figure: Figure 6: Single-person results We show qualitative results from ProsePose , HMR2 [], and HMR2-optim on complex yoga poses. Each example also shows the constraints that are predicted by the LMM at least $f=10$ times (and are thus used to compute $\mathcal{L}_{\text{LMM}}$) with their counts. ProsePose correctly identifies self-contact points and optimizes the poses to respect these contacts.

Baselines
We compare against the following baselines:

- •
HMR2 [] State-of-the-art pose regression method. We use this baseline to initialize our pose estimates for optimization.
- •
HMR2+opt Optimization procedure that is identical to our method without $\mathcal{L}_{\text{LMM}}$. We use this baseline as the default when the number of empty constraint sets is at least the threshold $t$.

Both the quantitative and qualitative results echo the trends discussed in the 2-person setting.
Table 4 provides the quantitative results.
The PCC metrics show that our LMM loss improves the predicted self-contact in complex yoga poses relative to the two baselines.
Figure 6 provides a qualitative comparison of poses predicted by ProsePose versus the two baselines. Below each of our predictions, we list the corresponding constraints predicted by GPT4-V. In each case, the predicted constraint captures the correct self-contact, which is reflected in the final pose estimates.
With the addition of the semantically guided loss, ProsePose effectively refines the pose to ensure proper contact between hand-foot or hand-hand, an important detail consistently overlooked by the baselines.

### 4.3 Limitations

While ProsePose consistently improves contact across settings and datasets, it has some limitations. First, though we mitigate it through averaging, LMM hallucination of incorrect constraints may lead to an unexpected output. Second, when taking the minimum loss across the possible chiralities of limbs, the pose initialization may lead to a suboptimal choice. We show in § 8.3
examples of failure cases like these. We also note that the LMM may be biased toward poses common in certain cultures due to its training data. In addition, we find that GPT4-V performs worse with some of the camera angles in the MOYO dataset (e.g. frontal or aerial), perhaps because in photos yoga poses are most often captured from a side view.

## 5 Conclusion

We present ProsePose , a zero-shot framework for refining 3D pose estimates to capture touch accurately using the implicit semantic knowledge of poses in LMMs.
Our key novelty is that we generate structured pose descriptions from LMMs and convert them into loss functions used to optimize the pose.
Since ProsePose does not require training, we eliminate the need for the expensive contact annotations used in prior work to train priors for contact estimation.
Our framework applies in principle to an arbitrary number of people, and our experiments show in both one-person and two-person settings, ProsePose improves over previous zero-shot baselines.
More broadly, this work provides evidence that LMMs are promising tools for 3D pose estimation, which may have implications beyond touch.

## 6 Acknowledgements

SS, EN, and TD were supported in part by the NSF, DoD, and/or the Berkeley Artificial Intelligence Research (BAIR) industrial alliance program.

## Appendix for Zero-Shot Pose Priors from Language

In this appendix, we provide additional details about our method (Section 7), details about metrics (Section 8.1), additional quantitative results (Section 8.2), examples of failure cases (Section 8.3), experiments with a different LMM (Section 8.4), and more qualitative comparisons (Section 8.5).

## 7 Additional Method Details

### 7.1 LMM Prompts

The box below contains our prompt for the two-person experiments.

The box below contains our prompt for the one-person experiment.

In each setting, the prompt is given as the “system prompt” to the GPT-4 API, and the only other message given as input contains the input image with the “high” detail setting.

### 7.2 Coarse Regions

Figure: Figure 7: Color-coded coarse regions in the two-person prompt: head, neck, chest, stomach, waist (back), waist (front), back, shoulder (back), shoulder (front), arm, hand, leg, foot, butt. Note that some of these regions overlap. For instance, the “back” includes the “waist (back)” and “shoulder (back)” regions as a subset.

Figure: Figure 8: Color-coded coarse regions in the one-person prompt: head, stomach, back, shoulder, arm, hand, leg, foot, butt. Note that the “chest,” “neck,” and “waist (front)” regions are not covered by the regions in the prompt, since they tend to have less importance for contacts in yoga poses.

Figure 7 illustrates the coarse regions referenced in the prompt in our two-person experiments. Figure 8 illustrates the coarse regions referenced in the prompt in our one-person experiments. In the one-person case, the prompt does not mention the “chest,” “neck,” or “waist” regions, since they tend to be less important for contacts in yoga poses, and the front/back shoulders are merged into one region, since the distinction tends to be less important for contacts in yoga poses.

### 7.3 Converting Constraints to Losses in 1 vs. 2 Person Cases

Our implementation of the conversion from constraints output by the LMM to loss functions differs slightly between the two-person and one-person cases.

#### 7.3.1 Two-person

Since we ask the VLM not to differentiate between “left” and “right” limbs, when there should be a constraint on both limbs (e.g. both hands), taking the minimum distance independently for each constraint pair may lead to a constraint on only one limb. Consequently, if the same body part (e.g. “hand”) is mentioned in at least two separate rows of the table output by the LMM (without any “left” or “right” prefix), we enforce that both the left and right limbs of this type must participate in the loss.

We also handle some variations in how the LMM references body parts. First, we check for the following terms in addition to the coarse regions named in the prompt: left hand, right hand, left arm, right arm, left foot, right foot, left leg, right leg, left shoulder, right shoulder, left shoulder (front), right shoulder (front), left shoulder (back), right shoulder (back), waist. “waist” corresponds to the union of “waist (front)” and “waist (back).” Each of these terms is mapped to the corresponding set of fine-grained regions, similar to the coarse regions shown in Figure 7. As stated in Section 3.3 of the main paper, if a “left” or “right” part is explicitly named by the LMM’s output, this part of the coarse region is directly used without considering the other part.

Second, we find there are some cases where the LMM expresses uncertainty between regions using a delimiter like “/” (e.g. “hand / arm”). So we split each entry in the Markdown Table’s output by the delimiter “/” and we compute the loss for each possible region that is listed; we then sum all of these losses.

#### 7.3.2 One-person

In the one-person experiment, we do not make use of the constraints involving the “ground” that the LMM outputs. Similar to the two-person case, the code for converting the LMM’s output to a loss function checks for the following terms in addition to the body regions listed in the prompt: left hand, right hand, left arm, right arm, left foot, right foot, left leg, right leg, left shoulder, right shoulder, left shoulder (front), right shoulder (front), left shoulder (back), right shoulder (back), waist
. Each of these terms is mapped to the corresponding set of fine-grained regions, similar to the coarse regions shown in Figure 7.

### 7.4 Bounding Boxes and Cropping

As stated in Section 3 of the main paper, we take bounding boxes of the subjects of interest as input and use them to crop the image in order to isolate the person/people of interest when prompting the LMM. For FlickrCI3D, we use the ground-truth bounding boxes of the two subjects of interest. For the other datasets, we use keypoints detected by ViTPose/OpenPose to create the bounding boxes. For the single-person MOYO dataset, we manually check that the bounding boxes from the keypoints and the selected HMR2 outputs correspond to the correct person in the image. We note that the baseline HMR2+opt also benefits from this manual checking, since HMR2+opt also depends on the HMR2 outputs and accurate keypoints.

## 8 Experiments

### 8.1 PCC Calculation

Figure: Figure 9: Color-coded 75 fine-grained regions used for PCC calculation

Figure 9 illustrates the 75 fine-grained regions used for PCC calculation, which are the same as those used in []. We opted to compute PCC on the fine-grained regions rather than on the coarse ones since prior work uses the fine-grained regions [] and since we want to measure contact correctness at a finer granularity (e.g. upper vs. lower thigh vs. knee). Since the regressors BEV and HMR2 use the SMPL mesh while the fine-grained regions are defined on the SMPL-X mesh, we use a matrix $M\in\mathbb{R}^{\text{num_vertices_smplx}\times\text{num_vertices_smpl}}$ to convert the SMPL meshes to SMPL-X in order to compute PCC.

**Table 5: Two-person Results. Per-person PA-MPJPE (lower is better). For FlickrCI3D, PA-MPJPE is computed using the pseudo-ground-truth fits.**
|  | Hi4D |  | FlickrCI3D |  | CHI3D |
| --- | --- | --- | --- | --- | --- |
|  | $\text{PA-MPJPE}_{\downarrow}$ |  | $\text{PA-MPJPE}_{\downarrow}$ |  | $\text{PA-MPJPE}_{\downarrow}$ |
| *Zero-shot* |  |  |  |  |  |
| BEV [] | 76 |  | 71 |  | 51 |
| Heuristic | 65 |  | 31 |  | 48 |
| ProsePose | 65 |  | 31 |  | 49 |
| *Supervised* |  |  |  |  |  |
| BUDDI [] | 70 |  | 43 |  | 47 |

### 8.2 Per-person PA-MPJPE

Table 5 shows the per-person PA-MPJPE for each of the datasets used in our two-person experiments.

### 8.3 Failure cases

Figure: Figure 10: Failure cases We show examples in which ProsePose fails to output a semantically correct pose. The constraints shown are the top 3 constraints (or the total number of constraints, whichever is smaller) that meet the threshold $f$ along with their counts ($f=1$ for two-person experiments and $f=10$ for the one-person experiment).

Figure 10 shows examples of two types of LingoPose failures: (1) incorrect chirality (example a) and (2) hallucination (examples b and c). In example (a), the top constraints are correct but without the chirality specified. The optimization then brings both hands of one person to roughly the same point on the other person’s waist, rather than positioning one hand on each hip. Similarly, both hands of the other person are positioned on the same shoulder of the first person. Examples (b) and (c) both show cases of hallucination. In example (b), the hand is predicted to touch the back rather than the hand. In example (c), the hand is predicted to touch the foot rather than the leg. Interestingly, in the yoga example, GPT4-V correctly predicts the name of the yoga pose in all 20 samples (“Parivrtta Janu Sirsasana”). However, it outputs a constraint between a hand and a foot, which is true in the standard form of this pose but not in the displayed form of the pose. Consequently, the optimization brings the left hand closer to the right foot than to the right knee.

### 8.4 Different Multimodal Model

In this section, we evaluate ProsePose when using a different LMM. We use LLaVA-NeXT 34B (i.e. LLaVA v1.6) [] as the LMM. We find that the model does not perform well in directly generating the table of constraints from the image. This is presumably a result of a weaker language model in LLaVA compared to GPT4 Therefore, we instead generate a caption from the LMM, and we feed the caption alone to GPT4 in order to convert it into a table of constraints.
We evaluated a few different prompts on the validation sets and chose the prompts with the best performance therein. For the two-person experiments, we use the following prompt for LLaVA:

We then use the following prompt with GPT4 to rewrite the caption so that it does not mention left and right to refer to limbs, since we find that the LMM is not reliably correct in doing so:

We then use the following prompt with GPT4 to create the formatted table.

For the one-person case, we use the following prompt for LLaVA:

We use the same prompt as above to rewrite the caption. We then use the following prompt to create the formatted table:

We use the gpt-4-0125-preview version of GPT4 via the OpenAI API (we obtained better results using this model than gpt-4-1106-preview).

**Table 6: LLaVA Results. Err denotes Joint PA-MPJPE for the two-person datasets (Hi4D, FlickrCI3D, CHI3D) and PA-MPJPE for MOYO. Lower is better for Err, and higher is better for Avg. PCC. Bold indicates best zero-shot method in each column.**
|  | Hi4D |  | FlickrCI3D |  | CHI3D |  | MOYO |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | $\text{Err}_{\downarrow}$ |  | $\text{Err}_{\downarrow}$ | $\text{PCC}_{\uparrow}$ |  | $\text{Err}_{\downarrow}$ | $\text{PCC}_{\uparrow}$ |  | $\text{Err}_{\downarrow}$ | $\text{PCC}_{\uparrow}$ |
| Heuristic | 116 |  | 67 | 77.8 |  | 105 | 74.1 |  | – | – |
| HMR2+opt | – |  | – | – |  | – | – |  | 81 | 85.2 |
| GPT4-V | 93 |  | 58 | 79.9 |  | 100 | 75.8 |  | 82 | 87.8 |
| LLaVA+GPT4 | 95 |  | 60 | 79.7 |  | 101 | 75.2 |  | 82 | 85.2 |

The latency of this approach is much higher than the single-stage approach used with GPT4-V, since we must feed each caption individually to the OpenAI API. Therefore, we set $N=5$ for these experiments.
Since we change $N$, we also need to select appropriate thresholds $f$ and $t$. As in the experiments with GPT4-V, we set $t=N$ for all datasets except CHI3D. For CHI3D, we find on the validation set that $t=2$ works better than $t=1$, so we set $t=2$. As in the experiments with GPT4-V, we set $f=1$ for the 2-person datasets, and we set $f=3$ for MOYO, to approximate the ratio $f/N$ used in the GPT4-V experiments. Finally, when converting the constraint pairs to loss functions, we found that on a small number of examples, the pipeline produced a large number of constraints, leading to very slow loss functions. Therefore, we discarded loss functions that are longer than 10000 characters.

Table 6 shows the results. On the 2-person datasets, the LLaVA+GPT4 approach performs better than the contact heuristic but not as well as GPT4-V. This is in line with holistic multimodal evaluations that indicate that GPT4-V performs better than LLaVA []. On the 1-person yoga dataset, the performance of LLaVA+GPT4 is comparable with that of the baseline (HMR2+opt). The reason that LLaVA performs worse than GPT4-V in this setting may be that LLaVA does not have enough training data on yoga to provide useful constraints.

### 8.5 Additional Qualitative Results

Figures 11, 12, 13, and 14 show additional, randomly selected examples from the multi-person FlickrCI3D test set. Figures 15, 16, 17, and 18 show the same examples comparing ProsePose with the pseudo-ground truth fits. Figures 19, 20, and 21 show additional, randomly selected examples from the Hi4D test set. Figures 22 and 23 show additional, randomly selected examples from the CHI3D validation set (which we use as the test set following []). Figures 24 and 25 show additional, randomly selected examples from the 1-person yoga MOYO test set.

Figure: Figure 11: Non-curated examples from the FlickrCI3D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 12: Non-curated examples from the FlickrCI3D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 13: Non-curated examples from the FlickrCI3D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 14: Non-curated examples from the FlickrCI3D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 15: Non-curated examples from the FlickrCI3D test set, comparing ProsePose with the pseudo-ground truth fits. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 16: Non-curated examples from the FlickrCI3D test set, comparing ProsePose with the pseudo-ground truth fits. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 17: Non-curated examples from the FlickrCI3D test set, comparing ProsePose with the pseudo-ground truth fits. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 18: Non-curated examples from the FlickrCI3D test set, comparing ProsePose with the pseudo-ground truth fits. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 19: Non-curated examples from the Hi4D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 20: Non-curated examples from the Hi4D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 21: Non-curated examples from the Hi4D test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 22: Non-curated examples from the CHI3D validation set (which we use as the test set). They are randomly selected from the examples for which there are at least nineteen non-empty constraint sets (since we set $t=2$ for CHI3D).

Figure: Figure 23: Non-curated examples from the CHI3D validation set (which we use as the test set). They are randomly selected from the examples for which there are at least nineteen non-empty constraint sets (since we set $t=2$ for CHI3D).

Figure: Figure 24: Non-curated examples from the MOYO test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

Figure: Figure 25: Non-curated examples from the MOYO test set. They are randomly selected from the examples for which there is at least one non-empty constraint set.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: See weakness
soundness: 3
strengths: - The paper is well written and flows well. The terminology and methodology is explained well
- Interesting use of LLMs: With the rise in LLMs, it is interesting to see vision problems being solved via language - especially for 3D computer vision and surpassing traditional methods. The authors have used LLMs to generate constraints for pose estimation.
- The proposed method performs well compared to traditional techniques.
summary: This paper presents ProsePose, a zero-shot framework that uses Large Multimodal Models (LMMs) to refine 3D human pose estimates, particularly for poses involving physical contact. The authors leverage the semantic knowledge embedded in LMMs to generate contact constraints from natural language descriptions, which are then used to improve 3D pose accuracy. ProsePose achieves competitive results against state-of-the-art models that require contact annotations, demonstrating its robustness on datasets with both self-contact (e.g., yoga) and person-to-person contact (e.g., dancing, tackling).
weaknesses: My main concerns with the paper are:
- It is not clear enough on why solving touch constraints help with better pose optimization. Are there other failure cases in pose optimization than touch constraints. I guess I am not clear on: If I want to use LLMs for better pose estimation - what is the first thing I ll ask LLM. Why is it contact information in this case? Why not some other information say visibility of regions/positioning of regions relative to others.
- What is the practical comparison on solving constraint optimization problem using LLM vs traditional methods. Is it more expensive in terms of compute, costs? Some discussion on this angle will be useful.
- There seem to be a lot of ablations that can be done on the prompting side which seem to be missing in the paper.
  - What are other ways to aggregate N responses from LLM? What if we take the intersection/union of constraints
  - What if I use different temperature for N different calls - will more randomness of LLM in different calls help solve the problem better?
  - How does the method perform if granularity of regions is different - other than SMPL-X
  - What if instead of asking constraints of all regions in one call - you use multiple calls say 10 regions per call. Does it perform any better?

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: See the weakness above.
soundness: 3
strengths: 1. The idea of using semantic guidance from LLM to refine the contact states of 3D human regression makes sense.   
2. The experiment results on Hi4D, FlickrCHI3D, and MOYO look promising.  The qualitative results look very encouraging.   
3. The writing is clear and easy to understand.
summary: This paper proposes an optimization method to improve the self-contact and close-interaction state of 3D human estimated from images, with the help of LLM contact descriptions. The motivation is that symantic guidance from LLM is very helpful to refine the contact states of single-image 3D human regression. Therefore, starting from 3D human predictions of existing HMR methods ,such as BEV, the proposed method jointly optimize: 1) the contact states based on LLM descriptions; 2) 2D alignment based on 2D keypoints; 3) Gaussian mixture pose prior and L2-norm shape constraints. The experiments are conducted on Hi4D, FlickrCI3D, and CHI3D. Compared with heuristic method, the  proposed method, ProsePose consitently improves PAMPJPE and PCC (percentage of correct contact). While increase the PAMPJPE on CHI3D, compared with baseline method, BEV.
weaknesses: 1. In Tab. 1, the performance degradation on CHI3D is confusing, because we may draw conflicting conclusion from the conflicting results. 
Therefore, to solve this confusing point, we may need further discussion in:   
a. a brief explanation of why introducing ProsePose makes the PA-MPJPE on CHI3D get worse.    
b. if there is a dataset-specific characteristics that might cause such performance degradation, digging into this point may provide an in-depth understanding of the proposed method.   

2. In some qualitative results, refining the contact also rectify the depth ordering between people. Detailed exploration at this might make the proposed method be more attractive. The analysis about this could be performed from two aspects:    
a. Quantitative evaluation of how ProsePose might be helpful in improving the depth ordering between people. Especially for the in-the-wild cases, evaluation on Relative human (proposed in BEV) dataset, might be doable during rebuttal, because the original predictions are already provided on its Github page.   
b. Qualitative results on challenging InterNet images might attract more readers to use the proposed method.    

3. Compared with BUDDI, the advantage is not quite obvious. More explaination about the comparison with BUDDI would be helpful for readers to understand. For instance, discuss and illustrate where ProsePose performs better or worse than BUDDI.

---

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: 1- Building on the previous comment, I think the arms and legs could be further divided into upper and lower segments, as LLMs should be able to distinguish these finer parts.

2- How consistent are the LLM predictions? In Fig. 5, the plots haven’t yet plateaued, suggesting there may still be room for improvement beyond $N=20$. I’m also curious whether it’s possible to obtain 20 unique responses from an LLM for a single image.

3- What is the proportion of images with an empty constraint set?

4- In Table 3, shouldn’t the setting without the LLM term be equivalent to the “Heuristic” baseline, as mentioned in the text (line 422)?
Also, there seems to be a typo in line 291, where a section number is referenced instead of the correct equation (Equation 3.1).
soundness: 2
strengths: This is a well-motivated and well-written systems paper. The inclusion of multiple components and stages makes for a challenging setup, yet the paper successfully scales the approach across multiple datasets, achieving performance that is either better than or on par with existing methods. Leveraging LLMs in a zero-shot setting for contact inference is a valuable contribution, automating the process and adding guidance during optimization. This approach appears especially effective in the two-person setting.
summary: The paper proposes leveraging Large Multimodal Models (LLMs) as a prior to improve self- and inter-person contacts in the task of 3D human pose estimation from RGB images. Specifically, given an image, the LLM is prompted to identify pairs of body regions in contact, which are then converted into vertex-based loss terms. The paper introduces a zero-shot learning setting in which no contact labels are required for training or fine-tuning. This approach extends fitting-based methods by incorporating LLM-guided contact terms into the optimization process. Evaluations on multiple datasets and across two scenarios—single-person self-contact and multi-person inter-person contact—demonstrate performance improvements over the baselines.
weaknesses: 1- Although the proposed zero-shot setting is intriguing at first, its practical applications are challenging to foresee. Despite the claims (line 553), the approach falls short of achieving accurate contact modeling in 3D reconstructions. It’s unclear whether language is a suitable modality to provide sufficient information for this task; in fact, its limitations are inherent. For instance, body segmentations must remain coarse due to natural language constraints, and when coupled with the proposed minimum distance loss between vertex sets (Eq. 3), the null space remains excessively large.

2- While the paper makes a small step in this direction, it lacks insights on how to further improve accuracy. Comparisons with prior work, such as BUDDI, suggest that labeled training data might be more effective for this task, or that the two approaches could potentially complement each other. A stronger setup would demonstrate incremental improvements over each baseline (e.g., BEV+ProsePose and BUDDI+ProsePose, as with Heuristic+ProsePose).

3- Although it addresses a different problem (human-object interaction), a prior work [1] leveraged language to infer contacts between body and object parts, which somewhat diminishes the novelty of this paper.

4- Additionally, a technical limitation lies in the hyper-parameters $N$, $f$ and $t$, which can significantly impact performance. While $N$ is ablated, there is no discussion around $f$ and $t$, or the robustness of the method across images.

[1] Wang, Xi, et al. "Reconstructing action-conditioned human-object interactions using commonsense knowledge priors." 2022 International Conference on 3D Vision (3DV). IEEE, 2022.

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 2
questions: Small Suggestions
1. The transitions between sentences could be a little smoother using proper conjunctions for a better reading experience, such as L46-47. 
2.. It would be more clear to illustrate by avoiding using super long sentences (L49-52, L76-79). 
3. The description of physical contact in the third paragraph of the introduction could be illustrated at L50-51 for better reading.
4. There is a duplicate reference in L687-695.
5. It is unnecessary to put the pose initialization in a sole subsection (Sec 3.2). Considering merging it to the problem definition and placing it in Sec 3.1.
soundness: 3
strengths: 1. The idea of extracting rich semantic pose prior from LLM to aid pose estimation is interesting and has great potential for future applications, including VR/AR, and metaverse. 
2. Experiment results demonstrate the effectiveness of the proposed method.
summary: This paper introduces a 3D pose estimation method leveraging the rich pose semantic prior inherent in LLM. It turns the natural language descriptions of physical interaction into tractable losses for constraining the optimization of 3D pose. The results are interesting, which pose a well estimate of complex scenes with several types of contact between individuals.
weaknesses: 1. The organization of the introduction makes it hard to follow.  It looks piecemeal, as the connection and split between the problem to be solved, the motivation, and the main insight are stiff. For example,  in L 56-57, existing methods struggle with solving the proposed problem, but what is the inner reason, why could it inspire the authors to use LLM? 
2. The ablation in Table 3 shows that the interpenetration loss (Eq.6) has a negative impact on the estimation results. Is there any possible explanation? Besides, there are no ablation results on PCC, which is also informative in evaluating the effectiveness of both L_LMM and L_p. 
3. I wonder if the proposed method can be applied to naturally taken photos without ground truth mesh. A few cases would be intuitive. 
4. Since all the losses applied in this method are either unsupervised or constrained on pseudo labels, an error correction mechanism during the optimization may be essential. Once the LLM outputs incorrect contact information, the optimization may lead to distorted mesh.