# Understanding technological challenges of motion capture hardware while animating avatars in performance

Anastasiia Ternova, Georges Gagneré

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Anastasiia Ternova, Georges Gagneré. Understanding technological challenges of motion capture hardware while animating avatars in performance. Journées d'informatique théâtrale, Oct 2024, Avignon, France. hal-05138748

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# UNDERSTANDING TECHNOLOGICAL CHALLENGES OF MOTION CAPTURE HARDWARE WHILE ANIMATING AVATARS IN PERFORMANCE

Anastasiia Ternova

Université Paris 8

anastasiia.ternova@gmail.com

Georges Gagneré

Université Paris 8

georges.gagnere@univ-paris8.fr

## ABSTRACT

Motion capture (mocap) is increasingly explored in the performing arts, yet its integration remains hindered by technical misunderstandings and the lack of a standardized methodology among artists. This article analyzes the challenges faced when using mocap technology to animate avatars in live performance contexts. It begins with a historical overview and a literature review to frame the evolution of mocap in the XXth and XXIst centuries. The study then categorizes and compares current mocap systems—optical, inertial, and AI-based—highlighting their respective strengths, weaknesses, and suitability for performance. Drawing from case studies and professional experiences, it discusses the gap between artists’ expectations and the technological realities, offering insights into device selection and usage. Ultimately, the article proposes a practical vocabulary and evaluative framework to assist artists in making informed choices and fostering effective collaboration with mocap specialists, aiming to support sustainable and creative integration of mocap tools in the arts.

## 1. INTRODUCTION

In studying the use of motion capture in the context of life performance in recent years, it becomes noticeably that many artists encounter a number of recurring problems. They can be summed up as the lack of technical understanding of the tools and an absence of a clear methodology of applying them in the field. These difficulties could slow down the creative process by forcing teams to give priority to resolving technical issues to the detriment of artistic ones. This could negatively impact the final output, but most importantly, such an unsatisfactory experience discourages artists from going further, exploring new territories, and creating more projects with the same instruments. So, in most cases, even the new solutions and developments made at the request of the performance artists remain in the framework of a specific project and are not further used, consequently there is no continuity and the growth of the domain becomes more complicated.

This article explores the tension between the artistic attractiveness of motion capture and its technical complexity which often remains obscure for theater-makers

and performers. It hypothesizes that the better understanding of functioning of different systems and devices might enrich creative imagination of artists and smooth the transfer of the technology into the performative arts. The article proposes a sort of a motion capture technical map, drawn from a point of view of an artist and researcher that theoretically could be applied for any sort of performance needs, acting methods and styles.

The state of the art section provides an overview of existing literature on the topic and defines motion capture.

The third chapter discusses the history of creation of the technology, its uses in the XXth and XXIst centuries, and its implementation in the performing arts.

The next chapter is dedicated to explanation of optical, inertial, video and AI systems and devices of motion capture that are accessible on the market nowadays. They are explained and compared in order to define by which parameters any device could be analyzed by the artists at the beginning of work.

The Expectations versus Reality chapter explores several modern cases of use of motion capture in performative arts and specifies which types of demand could artists have to the devices and which factors may make their experience satisfying or unpleasant.

The final part of this article aims to provide a proposal of a vocabulary that could help an artist to analyze any device and efficiently communicate with motion capture specialists and 3D-artists.

This paper is based on research, professional and artistic experience. Without giving a methodology aimed at a particular device, it seeks to lay down milestones for the strategy of choosing a device for any type of project and discusses the importance of adaptation of artistic intentions to technological constraints.

## 2. THE STATE OF THE ART

The motion capture or mocap in the field of entertainment was deeply explored in several major texts that could be conditionally divided into two parts according to time periods. In the first part, we can cite: "Understanding motion capture for computer animation" by Alberto Menache [36], "The animator's motion capture guide" by Liverman [35], "Mocap for artists" by Kitagawa and Windsor [33]
and "The mocap book" by Tobon [58], "Acting and performance for animation" by Hayes and Webster [20]. These texts belong to the first decade of the XXIst century, they cover history of motion capture, explain its complexity, and provide practical advices about using it for artistic purposes. Menache gives the richest details about mocap getting its place as a tool to animate avatars, also in the last re-edition of the book dated 2011 he wrote three appendices where he gives a list of motion capture devices and software manufactures, mocap service providers and finally online sources dedicated to mocap.

Though the above mentioned books are complementary and offer a lot of practical advices and a full-fledged vision of technical capacities of mocap in the beginning of the XXIst century, they are partly obsolete today and need a reconsideration, when applying to practice.

The second part of the texts belongs to the last 15 years of the XXI century, they are PhD scientific researches aimed to specific problematics, like merging the technology with acting, often addressing mostly the facial motion capture. To this part belong such texts as : "The cooling steam of the Polar Express : Historical origins, Properties and Implications of Motion capture" by Matthew Delbridge [13], "Performance driven facial animation with blend-shapes" by Shridhar Ravikamur [50], "Substitutive bodies and constructed actors : a practice-based investigation of Animation as performance" by Birgitta Hosea [22], "Acting and its double. A practice-led investigation of the nature of acting within performance capture" [32] by Jason Kennedy. Delbridge and Ravikamur are more directed to the technical solutions. Hosea, while trying to bring up practical acting approaches, has more theoretical thesis. Finally, Jason's work is balancing between technical and artistic approaches and shows an important experience in the field of motion capture and at the same time applies to the core of acting, including referring to Stanislavsky's system.

There were also several recent attempts of comparison of diverse motion capture systems and devices in scientific articles. In [49] written in 2018 a comparison between Mechanical, Magnetic and Optical systems could be found. But the paper is based exclusively on sources dated from 1998 to 2014 and besides considerably lacks information, for example in motion capture applications military use is not cited, inertial system is also ignored. In [59] article dated 2024 is dedicated to creation of digital version of Bulgarian folk choreographies using mocap tools and AI. Though the article sums up diverse technologies and proposes an interesting approach, the authors unfortunately, provide limited information (for example, they cite inertial Rokoko suit without mentioning that Rokoko also provides AI mocap tool and they don't specify which devices they use for their research). Therefor they cannot be used as a base of understanding of motion capture in arts.

Finally, in 2024 in Canada and France appeared three guides of motion capture, dedicated to artists, "Guide pour la démocratisation de l'utilisation de la capture de mou-

vement dans les arts vivants" <fnref n="1" /> [9] directed by Vincent Clement, Marion Cossin and Isabelle Domens and "Guide pour l'integration des technologies dans les arts vivants" <fnref n="2" /> [7] by Chloé Brindos, Marion Cossin and Isabelle Domens were both published by HUPR. <fnref n="3" /> The first document is based on a practical research in performative sphere and is aimed to give a general vision of motion capture diversity nowadays. It provides detailed information about different mocap approaches, points out their strong and weak sides, and gives links to different performative projects created with them. The second one is a general overview of integration of various technologies in performing arts with several guidelines.

The third document is called "Etude sur les dispositifs de capture de mouvement" <fnref n="4" /> [55] written by Anastasiia Ternova and revised by François Klein. This guide focuses on technical specification of diverse tools that could be found on market and it gives a feedback on how every tool might behave on stage. It also attempts to extract independent factors that might have a crucial influence on the quality of performance and that could become a sort of a check list for choosing a new instrument for a performance. The study is currently not publicly published.

These three texts could be considered as complementary but they are unfortunately available only in French, inaccessible to international audience.

Defining the notion of motion capture might be crucial for understanding its place on stage. The definition has varying over the last two centuries because it is a comparatively recent phenomenon that is constantly evolving. Today, most often, we refer to mocap as a process of recording physical motions of humans, animals and objects as mathematical data that could be visualized as a digital representation.

The most grounded definition was given by Ricardo Tobon who claims that "[...] motion capture is the acquisition of movement." [58] The most elegant one, in our opinion, was proposed by Alberto Menache in 2011 : "In brief, it is the technology that enables the process of translating a live performance into a digital performance." [37] This last statement reveals several outstanding particularities of mocap. Firstly, Menache replaces "process" with "technology" which refers to technological solutions that permit to take action. Secondly, he puts "translation" (in linguistic meaning) in place of "recording". This word gives us a key to understanding the complexity of the process behind the final result, unveiling the huge difference between the language of physical body and the language of computer. And it gives us a hint that, in the same way that a translation would never be exactly the same thing as the original text, motion capture output will never exactly reproduce the original movement. Finally, this last definition covers use cases where mocap would be used in real

<footnote n="1">¹. A guide for the democratization of the use of motion capture in the performing arts</footnote>

<footnote n="2">². Guide to the integration of technology in the performing arts</footnote>

<footnote n="3">³. Centre de recherche sur le potentiel humain/Human Potential Research Center</footnote>

<footnote n="4">⁴. Study on motion capture devices</footnote>
time without recording which opens up the motion capture to artistic and particularly performative explorations. So in this article speaking about mocap we will refer to Menache's definition.

### 3. MOTION CAPTURE, ANIMATING AVATARS AND PERFORMANCE

Back in 2008 Kitagawa and Windsor claimed that motion capture is not easily accessible to artists. They underlined the paradox: the public often meet CGI characters animated with this technology, but the process of animation with all its complexities remains obscure [33]. Although mocap tools become more and more user-friendly, the mentioned gap progresses as well. Indeed, the modern world of entertainment brings to life numerous fantastic 3D characters animated with mocap. In cinema we could see CGI avatars seamlessly tangled in realistic image and having natural interactions with real actors. In the video games the accurately performed acting transferred to avatars could bring the players a feeling of a profound, almost intimate connection with the story, like it is a case in such games as "The last of us" [43] or "Detroit. Become human" [47]. Naturally, the public, outsider to the creative process of those masterpieces tends to take motion capture for some kind of magic tool of instant animation, not being aware of the complexity of pre-, post- and production process, realized by armies of technologists, animators, actors and 3D-artists.

When each stage of animating avatars with motion capture is opened up, the complexity becomes obvious :

- **Conceptualization and creation of pipeline**: elaboration of precise and clear idea about the final results; of strategy about which tools and softwares will be used and how possible flaws would be overcome.

- **Session's preparation**: preparation of the mocap tools (that requires a deep understanding of how they work) or sometimes creation of new specific devices. Running test mocap sessions that will insure the proper functioning of all hard- and softwares and the compatibility between the mocap tools and the venue.

- **Motion capture session** that includes the reception of raw mocap data by dedicated software and a translation of those into a 3D skeleton-like structure. The moving skeleton that we will see in the software is a result of complicated invisible calculations done in real-time by the software. The positions, rotations and accelerations of each marker or sensor would be translated in numbers. In other words, it's a very accurate mathematical approximation. This step also demands a lot of awareness about the devices limits and attention from the actors and technical members of the team to provide high-quality data.

- **Motion retargeting** or a translation of the recorded movements from the initial "receiving" skeleton

to the skeleton of the final 3D-character (so the movement undergoes the second mathematical approximation). And cleaning of the final motions when we talk about pre-recorded movements and not live-time performing.

- **Integration of the avatar in the final 3D space** where we can appreciate the final result. This might sound like the quickest part of the process, but when it comes to pre-recorded movements, the visual adjustments and rendering might take as much time as first stages.

The misunderstanding of these backstage processes leads to unrealistic expectations and ignorance of actual motion capture limits and potential.

Going even further, it is necessary to mention that historically, mocap as a technology was not initially meant to animate 3D characters and started to be partly customized for these purposes just 40 years ago. But even in scientific papers it is still possible to find phrases like: "To speed up the animation process further, motion capture was invented" [49]. This citation highlights the tension between the attractiveness of motion capture and fundamental misunderstanding of the technology that is symptomatic today to the performative arts.

To analyze this deeper, a quick look at the history of motion capture and it's "marriage" with animation is necessary. Speaking about the origins, we traditionally go back to the end of the XIX century and quote five figures who were preliminary: Eadweard Muybridge, Étienne-Jules Marey, Dave and Max Fleischers and Harold Edgerton.

Muybridge developed a system of multiple cameras that permitted to capture and "cut" a rapid motion into frames and reproduce them at a different or same speed. Marey, among his other achievements, invented a special costume that "extracted" motion from its body. He dressed a person into a black tight-fitting suit with white dots and lines that marked joints and moving parts of the body which helped to create a static visualization of a motion, hiding its subject 1. Edgerton improved the use of the stroboscope. All these inventions formed a technical and theoretical basis for modern systems of motion capture, but also they shaped the application of the technology in the XXth century.

<figure><img src="image_2.png" /><figcaption>**Figure 1** – Movement extraction costume developed by Marey</figcaption></figure>
All three figures worked in profit of research on exploration and analyses of motion, so naturally next generations who developed their technologies later, in XXth century intended to follow the same path, making their way in military, medical, industrial, robotics engineering and sport fields.

Only brothers Fleishers' patent was destined directly to the artistic purposes and was designed to make the movements of cartoon characters more realistic, by manually retracing the acting of real performers, captured on film. The rotoscoping was a revolution for the world of cinema and demonstrated that a physical performance could become a core of animation of a 2D (and later 3D) character.

On the one hand, the principle of transposition of movement onto an imaginary character laid probably so far away from scientific decomposition of physical movement that the link between an artistic practice and technological achievements was not evident. On the other hand, the idea might exist "in the air", but the first 3D characters appeared only in 1982 in the short-film "Dream Flight" [6] directed by Philippe Bergeron and developed by Nadia and Daniel Thalmann [17]. Evidently, the animation of 3D avatars with motion capture was literally impossible before.

The first huge success of mixing CGI characters and mocap technologies came in 1985 with "Brilliance" [1]. In late 1984 Robert Abel was contacted by a creative director from Ketchum with a request to animate a robot for an advertisement of canned food. Though, the pioneer of visual effects and computer animation was convinced that technology was not ready for that challenge, he and his colleagues developed in a week a tool that made it possible to create a 30 second high-quality commercial that took 2 weeks and over 60 computers to render. The smoothly animated with motion capture sexy avatar (the official appeal that it got [33]) blew the mind of spectators and encouraged the entertainment industry to dive in this type of experimentations. Unfortunately, in cinema field, multiple of them failed, due to the improper use of tools (the mocap shooting for "Total Recall" [61] was done in bad conditions and the collected data was unusable), or weird motion retargeting that made avatars look strange and created a sort of Uncanny Valley effect [38] (case of "Sinbad: Beyond the Veil of Mists" [26] released in 2000). In the video-games sphere, the motion capture success was in a way more continuous, as it was used not to recreate precise, detailed performance of an actor, but to make fluid, realistic animations (case of FX Fighter, released in 1995). Surprisingly, theater came along and initially successfully integrated motion capture as well, in 1999 a choreographer Merce Cunningham, in collaboration with Shelley Eshkar et Paul Kaiser presented a performance "Biped" [11] where movements dancers performed next to non-realistic biped forms animated with motion capture.

Despite the fact that the first attempts of animating avatars with motion capture were made in the 1980s, the real popularity of this idea, notably in cinema and performative arts, came much later, in the XXIst century, with 2

important events: the release of "The Lord of the Rings" by Peter Jackson (2001 [23], 2002 [25] and 2003 [24]) and the production of Kinect in 2010. The first one demonstrated the potential of motion capture in animating avatars, the second one gave an widespread access to the technology. The team of "The Lord of the Ring" not only presented a high-quality animation of a realistic Gollum character made with motion capture, but also exposed the whole process to public, attracting the attention to its complexities and potential. This trilogy definitely stays at the first place in a long list of successful movies using motion capture to bring to life avatars. Kinect in its turn was a semi-optical devices, produced by Microsoft for gamers, it meant to motivate them to involve the whole body while playing. Kinect changed the landscape by becoming a cheap plug-and-play mocap tool that was the first one to give easy access to motion capture to non-professional users. Unexpectedly, it encouraged numerous artists to explore the technology and create various projects with it. Kinect 1 and 2 were not anymore produced since 2017, giving place to Kinect Azure, but in 7 years, Microsoft sold about 35 million Kinects all over the world, which definitely contributed to popularizing the idea of motion capture itself.

## 4. MOTION CAPTURE. SYSTEMS AND DEVICES

While researchers and cinema makers sometimes create their own mocap tools for specific purposes, in the performance domain, it is a common practice to turn to devices that already exist on the market.

For easier comprehension, we will separate notions of technology, system and device. If motion capture is a technology, a system would be a specific technical approach to it, and devices would be various tools belonging to a system. This article mainly targets devices produced by companies and accessible on the market.

There are numerous systems that approach motion capture from different angles: radio frequency positioning, electromechanical, digital armatures, optical, inertial, Artificial Intelligence, lidar, volumetric, pose estimation and others. Each of them is represented by a number of tools with their advantages and disadvantages. It is crucial to understand that there is no perfect device that would translate the whole complexity of physical movement into 3D without any flaws. Because firstly, any motion capture process is a double mathematical approximation: translation from physical "language" into "3D language" and then translation from one skeleton to another skeleton and, secondly, there is no device that would capture all parameters of physical body, its organs and systems at once, so in any case some of details will be lost. Therefore, each device corresponds to particular demands and it is a common practice to adapt it for specific projects. This also explains why systems are sometimes overlapped in search of a better quality. For example, inertial systems could use cameras in order to reduce drift-problems. And software of inertial and optical systems may integrate artificial intelligence to improve the quality of captured movement.
The four systems that are used the most commonly in performance for animating avatars are optical, video-based, inertial and AI. Here we will try to cover systems' functioning, tools' specifications and their compatibility with live performance.

## 4.1. Optical

The optical or marker-based system that was primary designed for medical applications [33] is based on a vision of multiple infra-red cameras placed all around the performance area. An actor has to wear a velcro suit, on which they will put reflective (passive) or emitting infrared light (active) markers. This system is considered to be the most reliable; the cameras provide an absolute reference that furnishes qualitative data about position of each marker at each frame. It is the best adapted system for capturing movements that require floor contact with other parts of body than feet, complicated acrobatic tricks, interactions between two or more subjects, animals' and objects' motions. We presume that going alongside medical and sport researches that required a lot of details of motion, optical system became the most optimal solution when the user looks for the most precise translation.

Optical system is often used for film and game industries, it is well suited for projects with long post-production process but could run into a number of obstacles when real-time is required, especially in theatrical context. Without taking in account the high cost that is rarely compatible with theater productions budgets the system could be considered as rigid. It takes a long time to setup. The cameras are usually sensitive for the changing of temperature, rarely adapted to sunlight or spotlights and entail an absence of reflective objects in the performing area. They need a recalibration if moved during a session. The space of acting is limited by the field covered by the cameras. The velcro costumes could be uncomfortable to wear during long sessions. The markers are required and must not be removed or occluded during the performance. All these factors involve high concentration from the performers and might limit creative expansion of light, costume and scenography designs.

Today on the market we can meet 4 major companies, all providing various motion capture devices all over the world and targeted to various purposes : Qualisys [46], Vicon [62], Optitrack [42] and Nokov [45]. Their cameras varies on lenses (that might decrease distortion of image), resolution, filters, tracking range, type of connection (and consequently quality of synchronization), latency, resilience to different types of challenges and some other specific features. All of them have series of cameras better adapted for enclosed spaces and series of cameras adapted for open ones. Qualisys and Nokov produce underwater mocap cameras, Optitrack and Nokov propose mini-devices for partial motion capture. Finally, Nokov company hits records by providing possibility to track up to 44 actors simultaneously (while most systems are usually limited to 5 actors max).

## 4.2. Video

Video-based system does not require markers and can operate either with stereo RGB cameras or an RGB camera combined with depth-camera. In the second case the depth-camera might be infra-red and works by principle of structured light, projecting outside a predefined pattern. The distortions of the pattern permit to calculate the distance to each object and that way translate a 2D image of RGB camera into 3D. In case of stereo-cameras, 2 RGB or more cameras will be used to imitate human vision and in that way triangulate a movement [9]. The prices are usually affordable, the hardware is usually easy to setup, and operate, the actor could wear casual clothes. But there are important limitations : the accuracy is much lower than with optical or inertial systems, the cameras are sensitive to the ambient light, the capture volume is highly limited, facing the camera while performing is required, certain type of clothes, overlapping and occlusions might cause a significant loss of quality. Generally, for complex projects it is more common to use several cameras or combine video-based mocap with AI.

Kinect by Microsoft is an example of combined video motion capture, it is easy to use and could be easily integrated in most of popular 3D softwares. But the performance space will be very limited, also the actor would be asked to face the camera most of the time that may considerably limit his or her expressiveness. The device would not work well outdoors and go "blind" with certain type of materials and colors. Nevertheless it is flexible for different types of needs, have low latency in real-time and could capture up to 5 persons at a time.

ZED [65] is a stereo camera produced by Stereo Labs, not specifically designed for motion capture, but can be used to this purpose. It also could be combined with AI tools for better quality. The software requires knowledge in coding so it might be slightly less accessible than Kinect for users with no programming background.

The researches is medicine prove that in some cases when non-invasive approach is needed video-based motion capture could be a more or less reliable alternative to highly-valued marker-based systems [60].

## 4.3. Inertial

Inertial or sensor-based motion capture was initially used for military purposes, in particular to control objects in unusual circumstances in real time. For example, it was implemented in the helmets of fight aviators to control the range of attack with the position of their head. Today inertial devices are usually composed of gyroscope, accelerometer and magnetic sensors that could define position of each body part and transmit it to the software via network, no cameras needed. Depending on a device the system might be more or less reliable in terms of accuracy but always has a drift problem (the skeleton would more or less slightly sliding away from the origin of the 3D world). Nevertheless, inertial mocap, being perfectly
adapted to complex environments could be a great solution for life performing.

It is an easily transportable system that is usually quick to setup, the devices could work in any type of venue, outdoors and inside. The performance could not be impacted by light or interaction with other actors or objects. The costume may not be always comfortable to wear during long sessions, but usually could be easily hidden. The tracking space is limited by the capacities of the Wi-Fi device. Also the cost of the most of devices is much more accessible for theater field.

Still, this system has some important weaknesses. Firstly and most importantly, the system is sensitive to magnetic fields and metallic objects that could influence the sensors and considerably decrease the quality of transmission. Secondly, it is not adapted for some specific kind of movements like acrobatics or contact with the floor with others parts of the body than feet. Finally, usually devices of this family need regular calibrations during the sessions, depending on device every 5 - 40 minutes.

Today on the market there are multiple companies that provide a diversity of inertial mocap suits. Their production varies on target users, price, manner of putting the sensors on the body (straps or costumes), capability of capturing fingers, batteries capacity, Wi-Fi capacity, FPS, presence or absence of protection from magnetic fields and drifts.

We can divide companies in 3 groups. First one is worldwide present and the most popular companies : Rokoko (producing Smartsuit) [52], Noitom (Perception Neuron suits) [44] and Movella (Xsens Awinda and Xsens Link suits) [41]. Rokoko's primal target is entertainment field, Noitom targets entertainment, sport and industry, finally Movella covers all areas and provides unique immune system for magnetic sensors, that makes their product the most stable and reliable.

The second group would be companies that are less known and usually accessible only locally : Synertial Suit (the UK) [3], The Shadow Suit (the USA) [39], Holosuit (India) [21], Vicon (the UK) [62], Teslasuit (the UK, Europe) [56].

Finally the third group is composed of "ghost" devices that were announced in 2024, but not released yet : Obskur (low-cost upper-body mocap device for streamers), Husky Sens (low-cost, full-body mocap device, developed in Latvia) and Mocopi (Japanese low-cost full-body mocap device that is meant to create an inertial alternative of Kinect) [55].

## 4.4. AI

Motion capture based on Artificial Intelligence, also called Markerless combines numerous complexes technologies, such as : advanced computer vision, pose-estimation, biomechanical modeling, deep learning etc. The principle of working is next : an AI system treats a single video or several synchronized videos of the same motion filmed from different angles, transforming the 2D movement into

3D. This system is highly attractive for its financial accessibility. It is used to analyze sport and more widely explored in medical researches, as it is beneficial in cases where the captured subject needs maintain the most natural state possible. One of the studies prove that Theia AI device could compete with Optitrack in calculating ankle and knee movements, but might get less precise while calculating hips position during fast movements [53]. Another medical research proves that markerless devices may outperform optical marker-based devices in some particular cases when patient wear loose garments, but nevertheless both systems lose quality when dealing with extra-baggy clothes [51]. While another [12] points out that AI results need to be filtered of divers bias, while optical mocap provides directly more precise data.

But as others it has its disadvantages : markerless motion capture usually requires very specific setup, to respect certain codes of clothing, needs to be trained to "translate" specific movements (like acrobatics or movements of a person in a wheelchair) and finally devices are precarious, as the competition is very high : today there are over 50 devices that exist on the market.

The variability of features is very large as well : price, open-source or not, quantity of captured persons, compatibility with infra-red cameras, presence or absence of such options as : live stream, fingers and facial performance capturing. Without being exhaustive, we could cite several devices that seem to be promising : Wonder Dynamics [5], RADiCAL [48], Capturey [8], Move IA [40], Theia [57], Vicon.

# 5. EXPECTATIONS VERSUS REALITY

But, as we said, a perfect device flexible enough for any purposes does not exist, so the practical evaluation will depend on two main points : adaptability of the tool for specific demands of the project and the consciousness of the artists of capabilities and flaws of that tool.

To better understand this complexity, we examine several use cases.

## 5.1. Inertial devices

When dealing with inertial system, Xsens Link outplays other devices on the lever of precision and stability, it also has highest FPS, it is easy to use and set up. It covers large spaces, is well adapted for long sessions and finally could be easily used in real time. Still, as it belongs to an inertial family, it has it flaws but in majority they are erased by corrections that are done in real time by the software.

Link was recently used in a VR-augmented dance performance "No Reality Now" [16], staged by Vincent DuPont and Charles Ayats in 2023 in France. The dancers movements were prerecorded and integrated in digital space. A hundred spectators were provided with VR headsets and invited to freely choose during the performance what to observe : the physical dancers or their alternative 3D ver-
sions. In his discussions with the public though, Vincent
often mentioned that initially the motion capture was meant
to work in real time, but the device was not ready for such
use. This seems confusing due to the fact that in 2016,
when Xsens Link was meant to be less developed, it was
used in real time in the performance "The Tempest" [14]
by Royal Shakespeare company co-created with The Ima-
ginarium Studios. Mark Quartley, playing Ariel was wea-
ring a suit during performance, present on stage both phy-
sically and digitally. And no annulation or postponing of
the performance due to motion capture issues were decla-
red. If we take a closer look to the "No Reality Now" case,
we could understand three main problems that prevented
the creative team to integrate the Xsens in real time. Two
of them are closely related to the functioning of inertial
system itself : firstly, there were several parts of the perfor-
mance where actors were supposed to crawl on the floor
which is a type of movement that is not well supported
by the inertial system. Secondly, there were two actors
who were meant to be captured in real time and to have
interactions in physical and virtual spaces, but with the
presence of drift, it was complicated to put in place and
no solution was found for this issue. Thirdly, the perfor-
mance meant overlapping of numerous complex lighting,
sound, and 3D-technologies. And particularly the VR se-
tup was completely innovative, using Wi-Fi controlling
method for the VR headsets that proved to be the most fra-
gile part of the technical setup, so adding motion capture
in real time would, destabilize the system and jeopardize
the touring.

On the other hand, Rokoko system objectively is easy
to setup and use, could cover large spaces and be adapted
for long sessions but is considerably unstable and inac-
curate. Logically, this seems to be less attractive tool for
live performing. In 2022 the the costume was used in real-
time to present the VR performance "Mandala, A brief
moment in time" [63] directed by Thomas Villepoux and
presented during the Venice Biennale. The actor in Paris
and the spectators in Venice were immersed together in a
digital world with a help of VR headsets. The actor was
leading the public through a story, talking to them, and
encouraging them to act. The performer was wearing a
Rokoko suit and could freely move in the space, while
the spectators were present in space as half-body spirits,
their head and body were tracked with the VR headsets
and the controllers. The Rokoko Smartsuit for the actor
was chosen for its financial accessibility, adaptability for
any space and capacity to make a day-long sessions, as
it works on rechargeable batteries. The imperfect mocap
quality was overtaken by the acting and proved to be en-
ough to create a playful plausible situation for the public.

Finally, an obsolete Perception Neuron 2 system that
still could be found on second hand market could cover
large spaces and be adapted for long live sessions, is re-
liable, mildly stable, but with a more complex setup that
often causes malfunctioning. For that reason users often
call it a highly capricious tool.

Still it was qualified as a "highly precise" in a research

about Chinese classical opera [64], where movements of
performers were captured and analyzed in order to im-
prove the learning of young actors.

In another creation-as-research, "The Wizard without Shadow" [54] produced by didascalie.net in France in 2021, Perception Neuron is used in real time for the workshops with children. A mocaptor [18] hidden behind backstage is animating a shadow- avatar, projected on a screen, a physi- cal actor playing in front of the screen leads the conversa- tion between the little spectators and the digital character. The children are invited to ask questions to the avatar and play games with it. At some point the mocaptor appears on the stage, revealing the technology behind the magic and letting the public explore the technical part of the per- formance. The fragility of the device is assumed by the artistic team, so the actors feel comfortable with possible bugs and could improvise around it if necessary. The qua- lity of the Perception Neuron have proved to be enough to create an engaging interaction with the audience.

So, as we can see, the final result of working with mo-
cap device in a performance, depends a lot on the initial
positioning of the team to its use.

5.2. Video-mocap devices

Kinect has low FPS and considerably limits mocaptors
expressiveness, but is successfully integrated into perfor-
mances and artistic experimentations by artists who as-
sume its limitations.

In the research of Luis Leite and Veronica Orvalho [34]
Kinect is described as an "ideal for working in non-prepared
environments and with non-expert artists". In their project
a mocaptor, hidden from the audience was performing in
real time different sport movements, retargeted to a plane
avatar and on a 3 dimension avatar and the public had to
guess which kind of sport was shown. In the VR perfor-
mance "The blinds" (2021) by INVIVO [15] a system of
multiple Kinects was used to track the movements of spec-
tators in real time. It seems that in both use cases the ar-
tistic teams were satisfied with the result.

5.3. Optical devices

Complex optical devices in their turn sometimes get al-
most negative reviews despite their undoubtedly quality. In
the creation as research of 2006 Jodi James, Todd Ingalls
and others integrated optical system used in real time into
a dance performance. Trying to deal with familiar to the
directors approach to the staging, the team encountered a
number of problems which resume in phrases : "This re-
quired us to find a balance between the factors related to
motion capture quality and the needs of lighting, projec-
tion, number of dancers, costuming, and clarity of prosce-
nium stage. This impacted many of the decisions we nee-
ded to make [...]".

On the opposite site, Gilles Jobin closely works with
Qualisys system since the pandemic period. He often talks
about the rich possibilities of 3D that introduce to live
performance things that are physically impossible. Since
2020 he created such performances as "Virtual Crossings"
[31], "Virtual Comedy - Piece multiusers" [30], "Real Time"
[28], "Reset! Beasts and demons" [29]. The sustainability
of his interaction with the technology shows his satisfac-
tion of work and curiosity to push the limits further.

Another bright example of integration of Optical devices in performance is researches of Stéphane Donikian and his collaboration with N+N Corsino company. In 2003 INRIA 5 posts an activity report [2] (in which Donikian was actively involved) where it described multiple experiments and research directions concerning VR, avatar's animations, real-time interactions and others. It is highly possible that these discoveries were lately applied in the performance of N+N Corsino "Seul avec loup" [10] 6 where the actors were supposedly pre-recorded with Vi-con system and then the animations were blended and controlled in real-time for reactive interaction with spectators.

**5.4. AI devices**

Pure AI motion capture use cases it is harder to track
and evaluate today due to the reduced quantity of such ex-
periences and the lack of sources about the use and the
creative process. Still, we can cite one of the most impres-
sive cases, the concert of Grimes during Coachella festival
in 2024 [9]. They used a complex set to create an aug-
mented double of Grimes that was partly animated with
pre-recorded animations and partly in real time with the
Move AI device, streaming to the Unreal Engine. Playing
with lights and fog machines, the creative team managed
to create an illusion of holographical presence of the ava-
tar. On the back-screen the performance was accompanied
with AI generated art. Analyzing the video-recordings, we
could suppose that 3D character might have some issues
with the floor contact, but it was smartly bypassed, by ma-
king it floating in the air; otherwise the system seems to
work well.

6. ARTISTIC CHECK-LIST

Kitagawa and Windsor suggest to "choose a right mocap expert" [33] when working with motion capture in an artistic project. It is definitely vital, but as we could see from the over-mentioned examples, understanding of devices could completely change the dialogue between the artists and the professional mocap makers and increase the satisfaction with the project in general [19].

Thus we need to find key notions that would give a por-
trait of any device and become a foundation of professo-
nal communication with specialists. The extreme speed of
technological evolution nowadays challenges us to search
for characteristics that would be applicable for any tool in-
dependently on its system. But also, basing on the analyze

of different performances, we will try to define how these
specifications could impact a performance.

In the Guide by Clement, Cossin and Domens, compa-
ring devices, they distinguish such features as : price, re-
quirement of markers or sensors, camera (no camera/single
camera/multiple cameras devices), computer performance
needed, user friendliness, indoor/outdoor use, precision,
precision for acrobatics movements, necessary brightness,
number of tracked people [9]. We think that this list is a
great basis, but it could be slightly transformed and com-
pleted with some more items. For example, they never talk
about the real-time animation which is a very attractive
feature for live-performances. So to search for the other
points we will address websites of mocap tools, YouTube
overviews, our own practical experience and analyze of
feedbacks of different performance-makers using motion
capture.

Each website dedicated to any device contains a lot of
information about its different qualities. If we try filter
them, searching for the details that would appear every
time regardless the mocap system, we may find such spe-
cifications as : price, FPS (frame per second), capture range,
latency, option of real-time animation, possible quantity
of mocaptors, feature of fingers' animation, compatibility
with different third-party softwares, output format. If we
turn then to our own practical experience and numerous
non-commercial youtube overviews on different devices,
we could get extensive insight into set up specificities and
visually analyze diverse output results and come up with
such terms as : time of setup, user friendliness, precision,
stability. Finally, if we check the feedbacks of users in the
performance field we will put attention to some features
that might not be always important to other fields, such as :
persistence of the device, time of setup, sustainability of
the session, discretion, compatibility with other elements
of scenography, group scenes etc.

Basing on all these parameters we will try now to pro-
pose a base of notions that could be helpful while choo-
sing a mocap device for a performance and we will try to
explain how each of them could impact particularly this
field. It might be practical to separate the notions on seve-
ral levels, depending on different types of needs.

The first one would be **Financial** :

1. **Cost of the hard and software** : today the devices of the highest quality cost much over 100 000 EUR. That might not be always accessible for performance-makers. The devices of highest quality are often developed for other industries, where over-detailed precision is necessary. Animation of avatars, especially in real time does not need this. But cheaper devices are often more capricious and impose more restrictions on the movements. It is also necessary to be aware that the license might be payable.

The second one concerns the **setup functions** :

1. **Time of setup** : as European companies often work on rented stages, the time of setup of the system could be a crucial point;

<footnote n="5">5. Institut national de recherche en sciences et technologies du nu-
mérique / French National Institute for Research in Digital Science and
Technology</footnote>

<footnote n="6">6. "Alone with wolf"</footnote>
2. **User friendliness of soft and hardware**: easy-to-use devices considerably cut the time of sessions and leave more space to artistic research;

3. **Sustainability of the session**: some tools are adapted only for sessions of 3 - 4 hours that would demand the artistic team a specific organization of working process;

4. **Adaptability for indoor/outdoor venues**: optional if outdoor performance is required;

5. **Capture range**: defines how much space will be available for the mocaptor to perform;

6. **Compatibility with other stage aspects**: in other words, how is the device adaptable with venue, light design of the performance, various elements of scenography, groups scenes etc.

7. **Discretion**: how much it is easy to hide it from public's eyes.

The third part would be **visual**:

1. **Real time animation**: necessary for pre-visualization, mocaptor training and real time performing;

2. **Drift**: unwanted displacement of an avatar in relation to the origin of the virtual space;

3. **Precision**: by this term, we mean the precision of transmission;

4. **Precision for complicated movements**: optional if specific (for example acrobatic movements) are needed;

5. **Stability**: for how long the precision of the tool will last;

6. **FPS**: the quantity of frames per second define the level of details of final animation;

7. **Finger animation**: optional feature for specific needs of a project;

8. **Possible quantity of mocaptors**: defines how many mocaptors could perform simultaneously.

The fourth part concerns the pipeline needs:

1. **Integrability** defines in which softwares the final animation could be streamed or imported. And the softwares in question will define the final 3D-image quality and possibility of real-time interactions with that image;

2. **Persistence**: the persistence of the device on the market will define how long it will be usable and compatible with other softwares and therefor could influence the life-time of the project itself.

In the artistic environment it is hard to create a tree-like decision tool, as the financial capacities, partnerships and needs may vary a lot. These notions could help an artist to understand which solution could fit creative needs. Here is an application of them to compare different motion capture systems.

In order to give an example of how evaluation could look like, the figures 2 and 3 provide a comparison of Optical, Inertial and AI systems, according to the given

<table><caption>**Figure 2** – Comparison of Optical, Inertial and AI systems. Part 1</caption><thead><tr><th></th><th>Optical</th><th>Inertial</th><th>AI</th></tr></thead><tbody><tr><th colspan="4">CATEGORIE I. Financial</th></tr><tr><td>Price<br>(up-to-date information for June 2025)</td><td>Depending on capture volume, could go higher then 100 000 EUR</td><td>From 3 000 to 46 000 EUR</td><td>From 0 EUR to 25 000 EUR and more</td></tr><tr><th colspan="4">CATEGORIE II. Set up functions</th></tr><tr><td>Time of setup</td><td>From multiple hours to multiple days</td><td>From 30 minutes to 1 hour</td><td>From 10 minutes to multiple hours</td></tr><tr><td>User-friendliness</td><td>Once cameras are installed the handling of hardware and software could be more or less easy</td><td>More or less user-friendly depending on device</td><td>Certain devices are intuitive, others require knowledge in coding and high carefulness to the details while taking video or operating system in real time</td></tr><tr><td>Time of session</td><td>Multiple hours in case if space has stable temperature</td><td>Depending on device, up to multiple hours.</td><td>From 5 seconds (limit time for certain devices) to several hours depending on cameras' batteries</td></tr><tr><td>Indoor/outdoor</td><td>Perfect for indoor, special cameras are needed for outdoor capture</td><td>Easily adaptable for indoor and outdoor use</td><td>Most devices are perfect for indoor performance, outdoor possible if certain conditions are met.</td></tr><tr><td>Capture range</td><td>Depending on quantity of cameras</td><td>From 4m2 to 150m2 and more depending on Wi-Fi router</td><td>Depending on quantity of cameras</td></tr><tr><td>Compatibility</td><td>In most cases the stability of cameras, specific lighting, stability of temperature, and absence of marker-occlusions are required</td><td>Requires space with absence of strong magnetic fields</td><td>In most cases the stability of cameras, homogeneous background, specific lighting and absence overlappings are required</td></tr><tr><td>Discretion</td><td>Cameras could be hidden, but markers need to be visible</td><td>If device is highly stable it could be hidden under a costume</td><td>System usually requires tight-fitting clothes contrasted with the background</td></tr></tbody></table>

<table><caption>**Figure 3** – Comparison of Optical, Inertial and AI systems. Part 2</caption><thead><tr><th></th><th>Optical</th><th>Inertial</th><th>AI</th></tr></thead><tbody><tr><th colspan="4">CATEGORIE III. Visual</th></tr><tr><td>Real-time</td><td>Easily adapted for real-time</td><td>Easily adapted for real-time</td><td>Some devices do not work in real time</td></tr><tr><td>Drift</td><td>Absent</td><td>Present, but could be reduced in certain systems with AI treatment or adding a camera</td><td>Absent, but multiple devices have severe problem of hips-stability and floor-contact</td></tr><tr><td>Precision</td><td>Highly precise</td><td>Precision might degrade during a session</td><td>Devices that use one camera inputs might produce lower quality then multiple-camera inputs</td></tr><tr><td>Precision for complicated movements</td><td>Highly precise</td><td>Flaws are recurrent when multiple floor contact with hands, hips etc. are present</td><td>Most systems might meet problems interpreting unusual movements (acrobatics, movements of people with disabilities)</td></tr><tr><td>Stability</td><td>Highly stable</td><td>Depends on device</td><td>More stable if multiple-cameras input is used</td></tr><tr><td>FPS</td><td>Kinect: 30 FPS<br>Other systems: from 100 to 1000 FPS</td><td>From 60 to 240 FPS depending on device</td><td>From 30 to 240 FPS</td></tr><tr><td>Finger animation</td><td>Accessible, but might require additional equipment</td><td>Accessible, but might require additional equipment</td><td>Inaccessible in certain devices, easily accessible and qualitative in others</td></tr><tr><td>Facial animation</td><td>Accessible, but might require additional equipment</td><td>Accessible, but might require additional equipment</td><td>Inaccessible in certain devices, easily accessible and qualitative in others</td></tr><tr><td>Mocaptor quantity</td><td>Usually from 1 to 5; up to 44 for Nokov</td><td>In most cases from 1 to 5</td><td>In most cases from 1 to 4, some devices could capture up to 8 - 10 people</td></tr><tr><th colspan="4">CATEGORIE IV. Pipeline needs</th></tr><tr><td>Integrability</td><td>Most devices have plugins for numerous softwares, notably for Unity, Unreal Engine, Motion Builder and Maya.</td><td>Most devices have plugins for numerous softwares, notably for Unity, Unreal Engine, Motion Builder and Maya.</td><td>Depends a lot on device</td></tr><tr><td>Persistence</td><td>Most companies have over 25 years story</td><td>Most companies have over 10 years history</td><td>Multiple devices are from 1 month to 8 years old</td></tr></tbody></table>
<table><caption>Figure 4 – Comparison of Inertial devices. Part 1</caption><thead><tr><th></th><th>XSens</th><th>Perception Neuron</th><th>Rokoko</th></tr></thead><tbody><tr><th colspan="4">CATEGORIE I. Financial</th></tr><tr><td>Price<br>(up-to-date information for June 2025)</td><td>Hardware: No official information, contact the company for prices.<br>Software: No official information, contact the company for prices, licence required for recording and streaming<br>Might be around 6 000 EUR and much higher</td><td>Hardware: No official information, contact the company for prices/<br>Software: free, but licence key is required<br>Might be around 5 000 EUR and much higher</td><td>Smartsuit: €2,295<br>Software: between 20 EUR and 70 EUR per month</td></tr><tr><th colspan="4">CATEGORIE II. Set up functions</th></tr><tr><td>Time of setup</td><td>From 20 to 30 minutes</td><td>From 30 minutes to 1 hour</td><td>From 10 minutes to multiple hours</td></tr><tr><td>User-friendliness</td><td>Easy to use<br>Disabling of computer firewall is required</td><td>Generally easy to use but<br>• the hardware is highly sensitive to magnetic fields<br>• disabling of computer firewall is required<br>• frequent recalibrations (every 5 - 15 minutes) might be needed</td><td>Generally easy to use but<br>• the suit might meet occasional problems with connection to the software<br>• handling a Wi-Fi router is needed<br>• disabling of computer firewall is required<br>• frequent recalibrations (every 4 - 10 minutes) might be needed</td></tr><tr><td>Time of session</td><td>Awinda: sensors work on integrated batteries, between 4 and 6 hours<br>Link: hardware works on external batteries, produced by Movella, one batterie could provide a session of about 7 - 8 hours, working with several full-charged batteries could extend the session time</td><td>Perception Neuron 3: sensors work on integrated batteries, from 3 to 5 hours<br>Perception Neuron Pro: sensors work on integrated batteries, from 3 to 5 hours</td><td>The hardware works on external batterie, that could be replaced at any time. With several batteries of high capacities the session could last more then 8 hours</td></tr><tr><td>Indoor/outdoor</td><td>Adapted for both</td><td>Adapted for both</td><td>Adapted for both</td></tr><tr><td>Capture range</td><td>Awinda: works with network provided by Movella, about 50m2<br>Link: works with a Wi-Fi router provided by Movella, about 150m2, could also work with other routers</td><td>Perception Neuron 3: 4m2<br>Perception Neuron Pro: 7m2</td><td>Fully depends on Wi-Fi router chosen by user</td></tr><tr><td>Compatibility</td><td>Preferably requires space with low magnetic interferences</td><td>Strictly requires space with low magnetic interferences</td><td>Strictly requires space with low magnetic interferences</td></tr><tr><td>Discretion</td><td>Could be hidden under a costume</td><td>Could be hidden under a costume</td><td>Could be hidden under a costume</td></tr></tbody></table>

<table><caption>Figure 5 – Comparison of Inertial devices. Part 2</caption><thead><tr><th></th><th>XSens</th><th>Perception Neuron</th><th>Rokoko</th></tr></thead><tbody><tr><th colspan="4">CATEGORIE III. Visual</th></tr><tr><td>Real-time</td><td>Perfectly adapted, but the calibration takes several minutes and the captured person have to walk around</td><td>Well-adapted, but calibrations require some unusual poses</td><td>Well-adapted, the calibration requires one pose that could be easily masked during performance</td></tr><tr><td>Drift</td><td>Awinda: Present but much lower then with other devices<br>Link: present but lower then Awinda<br>Might be partially reduced, using vive-trackers (feature available to certain type of licence)</td><td>Present</td><td>Highly present<br>Might be partially reduced with Coil Pro option €2,995<br>(up-to-date information for June 2025)</td></tr><tr><td>Precision</td><td>Highly precise</td><td>Precise when well-calibrated</td><td>Precise, but unstable</td></tr><tr><td>Precision for complicated movements</td><td>Might have slight problems with capturing atypical floor contacts (hands, hips, head etc.)</td><td>Might have severe problems with capturing atypical floor contacts (hands, hips, head etc.)</td><td>Might have severe problems with capturing atypical floor contacts (hands, hips, head etc.)</td></tr><tr><td>Stability</td><td>Highly stable, one calibration could be efficient for 30 minutes - 1 hour of session</td><td>Instable, frequent recalibrations might be required (every 5 - 20 minutes)</td><td>Highly instable, frequent recalibrations are required (can go up to every 4 - 5 minutes)</td></tr><tr><td>FPS</td><td>Awinda: 60 FPS<br>Link: 240 FPS</td><td>Perception Neuron 3: 60 FPS<br>Perception Neuron Pro: up to 240 FPS</td><td>200 FPS</td></tr><tr><td>Finger animation</td><td>Requires special equipment, notably Manus Gloves: about 5 000 EUR (information from the official site in 2024, not available anymore) or StretchGloves: about 2 000 EUR (automatically integrated to the XSens software)</td><td>Perception Neuron 3: Available with additional equipment, Perception Neuron 3 finger sensor kit<br>Perception Neuron Pro: Available with additional equipment, Studio Motion Capture Gloves</td><td>Possible with SmartGloves: €1,795<br>The quality of finger animation is reported to be good<br>(price up-to-date information for June 2025)</td></tr><tr><td>Facial animation</td><td>Not included</td><td>Available with Axis Face (about 400 EUR), real-time is yet impossible. Last official updates on the application: 2024<br>(price up-to-date information for June 2025)</td><td>Available with Rokoko face capture, real-time possible.<br>Company also provide a comfortable head-rg (295 EUR) (price up-to-date information for June 2025)</td></tr><tr><td>Mocessor quantity</td><td>Depending on licence from 1 to 4</td><td>Up to 5</td><td>3</td></tr><tr><th colspan="4">CATEGORIE IV. Pipeline needs</th></tr><tr><td>Integrability</td><td>Plugins for: Maya, Motion Builder, Blender, Unity</td><td>Plugins for: Maya, Motion Builder, Unity, Unreal Engine, Blender, Houdini, Cinema 4D, Reallusion</td><td>Plugins for: Maya, Motion Builder, Unity, Unreal Engine, Blender, Houdini, Cinema 4D, Iclone 8, Inch</td></tr><tr><td>Persistence</td><td>Founded in 2000</td><td>Founded in 2012</td><td>Founded in 2014</td></tr></tbody></table>

mocap attributes. The figures 4 and 5 in their turn, are specifically focused on major Inertial devices mentioned in the part 4.3.

## 7. CONCLUSION

The process of integration of motion capture into performance industry is fascinating and might evolve in the most unexpected directions. We have not touched here other cases than animating avatars, though other cases of mocap implementation are interesting as well (e.g. interacting with objects).

This article provides a prototype of motion capture devices evaluation, that is based on analyze of diverse scientific papers, technical and artistic guides, our professional experience. This evaluation is meant to be sustainable and adaptable to any demands, but of course, it is a sort of hypothesis, that needs to be proved. If we could propose it to performer makers and motion capture specialists in creative process it would be a perfect setting to put this theory on a test. As the next step, we need to get professional critics in order to develop, precise and polish these hypotheses.

Section 3 of the article presented different stages of motion capture and we have barely developed only the first one, the pipeline, and more precisely the strategy of choosing a correct device. To elaborate the understanding of animating avatars with motion capture in performance further, it is obviously necessary to explore all other stages.

All these topics promise most interesting research and discussions.

## 8. REFERENCES

[1] Abel R. and associates *Brilliance*, Ketchum Advertising, 1982.

[2] Activity Report *Project-Team siames Image Synthesis, Animation, Modeling and Simulation*, INRIA, Rennes, 2003.

[3] AiQ Synertial Synertial, https://www.synertial.com/animation (accessed on 30/04/2025).

[4] Argonaut Software/GTE Entertainment *FX Fighter*, MS-DOS, 1995.

[5] Autodesk Company, *Wander Dynamics*, https://wonderdynamics.com/, (accessed 30/04/2025)

[6] Bergeron P., Thalmann N., Thalmann D., *Dream Flight*, Université de Montréal, 1982.

[7] Brindos C., Cossin M., Domens I. *GGuide pour l'integration des technologies dans les arts vivants*, HUPR, 2024.

[8] Captury Captury, https://captury.com/, (accessed on 30/04/2025).

[9] Clement V., Cossin M., Domens I. *Guide pour la démocratisation de l'utilisation de la capture de mouvement dans les arts vivants*, HUPR, 2024.
[10] Corsino N. and N. *Seul avec loup*, N+N Corsino company, Paris, 2006.

[11] Cunningham M., *Biped*, University of California, CA, 1999.

[12] Das, K., de Paula Oliveira, T., Newell, J. "Comparison of markerless and marker-based motion capture systems using 95 percent functional limits of agreement in a linear mixed-effects modelling framework." *Scientific Reports*, 2023, 13(1), 22880.

[13] Delbridge M. The cooling steam of the Polar Express : historical origins, properties and implications of performance capture, Queensland University of Technology, 2014.

[14] Doran G., *The Tempest*, The Royal Shakespeare Company and The Imaginarium Studios, London, 2016.

[15] Dubuc J., *Les Aveugles*, Collective Invivo, France, 2021.

[16] Dupont V., Ayats Ch., *No reality now*, J'y pense souvent, Dark Euphoria, Lyon, 2023.

[17] Gagneré, G., "Directing Avatars in Live Performances - an Autonomy Simulacrum of Virtual Entities", in *Live Performance and Video Games, dir. R. Dreifuss, S. Hagemann, I. Pluta, transcript Verlag, 2024, 978383767175, p. 87-103*

[18] Gagneré, G., Plessiet C., "Experiencing avatar direction in low cost theatrical mixed reality setup", in *Proceedings of the 5th International Conference on Movement and Computing (MOCO '18), Association for Computing Machinery, New York, NY, USA, 2018, Article 55, 1-6*

[19] Gagneré G., Ternova A. "AKNRegie : bridging digital and performing arts", in *Proceedings of the 20th International Conference on Culture and Computer Science : Code and Materiality (KUI '23), Lisbon, Portugal, ACM, New York, NY, USA, 2023, 11 pages*

[20] Hayes D., Webster C. *Acting and Performance for Animation*. Routledge, 2013.

[21] HoloWorld, *Holosuit*, https://www.holoworld.one/holosuit (accessed on 30/04/2025).

[22] Hosea B. Substitutive bodies and constructed actors : a practice-based investigation of animation as performance, University for the Creative Arts, 2012.

[23] Jackson P., *The lord of the rings : The Fellowship of the ring*, New Line Cinema, WingNut Films, 2001.

[24] Jackson P., *The lord of the rings : The Return of the King*, New Line Cinema, WingNut Films, 2003.

[25] Jackson P., *The lord of the rings : The Two Towers*, New Line Cinema, WingNut Films, 2002.

[26] Jacobs., Ricks E. *Sinbad : Beyond the Veil of Mists*, Improvision Corporation and Pentafour Software, 2000.

[27] James J., Ingalls T., Qian G. and co. "Movement-based Interactive Dance Performance", *Proceedings of the 14th ACM international conference on Multimedia, 2006*.

[28] Jobin G., *Real Time*, Cie Gilles Jobin, Switzerland, 2020.

[29] Jobin G., *RESET! Beasts and Demons*, Cie Gilles Jobin and Studio44MocapLab, Geneva, 2024.

[30] Jobin G., *La Comédie Virtuelle – Live Show*, Cie Gilles Jobin, Geneva, 2024.

[31] Jobin G., *VIRTUAL CROSSINGS*, Cie Gilles Jobin, Geneva, 2020.

[32] Kennedy J. Acting and Its Double : a Practice-Led Investigation of the Nature of Acting Within Performance Capture, Auckland University of Technology, 2021.

[33] Kitagawa M., Windsor B. MoCap for artists : workflow and techniques for motion capture. Elsevier Inc, 2008.

[34] Leite L., Orvalho V. "Inter-Acting : Understanding interaction with performance-driven puppets using low-cost optical motion capture device", *International Journal of Advanced Computer Science, Vol. 3, No. 2, Pp. 65-69, Feb., 2013*.

[35] Liverman M. *The Animator's Motion Capture Guide : Organizing, Managing, Editing*. Charles River Media, 2004.

[36] Menache A. *Understanding Motion Capture for Computer Animation and video games*. Morgan Kaufmann Publishers Inc., 1999.

[37] Menache A. *Understanding Motion Capture for Computer Animation and video games*. Elsevier Inc., 2011.

[38] Mori M. "he Uncanny Valley", *Energy*, 7(4), 33–35, 1970

[39] Motion Workshop *Shadowsuit*, https://www.motionshadow.com/ (accessed on 30/04/2025).

[40] Move AI Ltd, *Move AI*, https://www.move.ai/ (accessed on 30/04/2025).

[41] Movella Inc., *Xsens*, Website Name, URL (accessed on Date) https://www.movella.com/products/motion-capture-suits, (accessed on 30/04/2025).

[42] NaturalPoint, *Optitrack*, https://www.optitrack.com/cameras/ (accessed on 30/04/2025).

[43] Naughty Dog/Sony Computer Entertainment, *The Last of Us*, Play Station 3, 2013.

[44] Noitom LTD, *Perception Neurons*, https://www.noitom.com/perception-neuron-series (accessed on 30/04/2025).

[45] Nokov Motion Capture, *Nokov*, https://en.nokov.com/direct (accessed on 30/04/2025).
[46] Qualisys Qualisys, https://www.qualisys.com/cameras/ (accessed on 30/04/2025).

[47] Quantic Dream/Sony Interactive Entertainment[a] Detroit : Become Human, Play Station 4, 2018.

[48] RADiCAL Solutions, Inc. RADiCAL, https://radiomotion.com/ (accessed on 30/04/2025).

[49] Rahul, M. "Review on motion capture technology" Global journal of computer science and technology, 18(1), 2018, 23-26.

[50] Ravikumar S. Performance Driven Facial Animation with Blendshapes, University of Bath, 2017.

[51] Ray, L. S. S., Zhou, B., Suh, S., Lukowicz, P. "A comprehensive evaluation of marker-based, markerless methods for loose garment scenarios in varying camera configurations", *Frontiers in Computer Science*, 2024, 6, 1379925.

[52] Rokoko Rokoko, https://www.rokoko.com/fr/products/smartsuit-pro (accessed on 30/04/2025).

[53] Song, K., Hullfish, T. J., Silva, R. S., Silbernagel, K. G., Baxter, J. R. "Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements" *Journal of biomechanics*, 2023, 157, 111751.

[54] Ternova, A., Gagneré, G., "The Wizard Without Shadow : Staging and Evolution of a Performance Involving Motion Capture", *International Journal of Art, Culture, Design, and Technology (IJACDT)*, vol.11, no.3, 2022, p.1-13.

[55] Ternova A., Klein F. *Etude sur les dispositifs de capture de mouvement*. To be published.

[56] TESLASUIT, Deep Divers Ltd Teslasuit, https://teslasuit.io/products/teslasuit-4/ (accessed on 30/04/2025).

[57] Theia Theia Markerless Motion capture, https://www.theiamarkerless.com/ (accessed on 25/06/2025).

[58] Tobon R. *The Mocap Book : A Practical Guide to the Art of Motion Capture*. Foris Force, 2010.

[59] Valcheva, D., Kalushkov, T., Shipkovenski, G. "Research on Motion Capture Technologies and AI Video Synthesis for Creating Digital Bulgarian Folk Choreographies" *BRAIN. Broad Research in Artificial Intelligence and Neuroscience*, 16(1 Sup1), 2025, 117-126.

[60] Valenzuela, R., Corral, J., Diez, M., Provot, T., Campa, F. J., Herrero, S., ... Pinto, C. "Validation of a markerless motion capture system for centre of mass kinematic analysis" *Biocybernetics and Biomedical Engineering*, 45(2), 2025, 278-286.

[61] Verhoeven P., Total Recall, Carolco Pictures, 1990.

[62] Vicon Motion Systems Ltd UK Vicon, https://www.vicon.com/hardware/ (accessed on 30/04/2025).

[63] Villepoux T., Mandala, A brief moment in time, Digital Rise and Sandman Studio, Paris, 2022.

[64] Wang T.-J. "A study on utilizing 3D motion-capture analysis to assist in Chinese opera teaching", *Presearch in Dance Education* 23, 2 (2022), 194-222.

[65] ZED 2 camera, StereoLabs Inc. ZED, https://www.stereolabs.com/en-fr/products/zed-2 (accessed on 25/06/2025).