This contrasts with the more positive reactions from early tests of the motion capture that were shown informally to viewers, where only a short motion captured segment was shown in isolation. Tinwell et al., note that the level of uncanniness perceived by viewers is influenced by the emotion being displayed, for example, “With regards to sadness, despite the fact that removing upper facial animation in the virtual character led to lower familiarity and human- likeness ratings, participants rated both fully and partially animated virtual characters expressing sadness as comparatively less uncanny than when exhibiting any other emotion” (2011a, p.747). Tinwell et al attribute this to a natural tendency of viewers to anthropomophize cartoon characters. Tinwell et al were working with human and realistic characters, whereas my computer generated character was deliberately stylized and delivered pre-
anthropomorphized. However, it may be that the choice of the expression of sadness could be considered less of a difficulty for a CG character than other emotions. Likewise, Tinwell et al found that happiness, in particular a lack of upper face moment, was less of a trigger for uncanniness for viewers, provided the mouth was smiling (2011a). Ultimately, it is difficult to compare the results of the two approaches as Tinwell et al’s, research measured the reaction of human faces, comparing live action actors with a computer generated faces. With my artifact, though it swaps out between live action footage, the comparison is with a stylized,
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motion captured scenes when they saw them in isolation, in particular finding the body movements more absorbing. As soon as the motion captured scenes were placed next to live action footage, it became harder for viewers not to make direct comparisons and thus find the computer-generated footage lacking. The body movements became overlooked and the lack of facial animation became more of an issue.
In light of these results, and to encourage the viewer to look at subtle body movements, rather than facial expressions, further research should explore:
1. Short (30 second only) clips of a motion captured avatar, without other forms of animation or live action footage to distract the viewer.
2. A redesign of the character. While non-human and stylized, the 3D avatar was still too realistic and her lack of facial expression dropped her into the uncanny valley. I would suggest a design of a softer, fluffier character such as a soft toy or sock puppet transposed into motion captured movement, where viewers are not expecting or looking for realistic facial expression.
3. Motion capturing facial expression. However, this is a more complex approach requiring more complex software, and might prove to be still very difficult to capture the full range of human expression successfully without hand tweaking the motion on top of the motion capture. The problem would remain that viewers would look to the face before studying the body.
6.3.2 Rotoscoping
As the rotoscoped sequences appeared to work quite well in getting viewers to focus and observe facial expression, further work might involve:
1 Short rotoscoped clips of real (not acted expressions.)
2. Rotoscope played without voiceover as well as with voiceover, to see how much (if any of a different reaction this prompts, and if it encourages viewers to look more closely) an option might be to start without sound then blend in sound, to see if the viewers interpretation of the emotion conveyed matches up.
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3. Cut with live action footage. For example, beginning with a live action close up, blending to a rotoscoped version and then blending back to a live action close up. This to see if the blending helps viewers continue to focus on the live action face to trace the details previously highlighted by the rotoscoped lines.
A difficulty of the rotoscoped experiment is that is does require the time and skill of a trained artist to interpret and hand animate the emotions by picking them out from each frame. The success of the animation, as a medium to highlight the communication of a subject is dependent on how good the animator might be at interpreting and then expressing emotion, and there is the danger that the animator might be adding to, subtracting from or distorting the data of the facial expressions. For an example of the variation you might expect from such an exercise, see Sabistion’s film Roadhead (1998) for an excellent example of a wide range of animators’ approaches to the same documentary live action footage. How much this might be considered a problem might be dependent on the subject matter of the animation. A more light- hearted or abstract narrative might benefit the viewer reinterpreting away from the original aim of the piece. However, in the case of a more serious animated documentary illustrating sensitive themes, confusing, reinterpreting or muddying the message could be problematic.
6.3.3 Hand-drawn animation
Part of the aim of this research was self-reflection, refining of skills and the journey taken by the researcher themselves, as animator, researcher and auteur, and as a possible methodology for unlocking insights. While the hand drawn elements were entertaining and clearly
understandable for the majority of the viewers who responded, they did not appear to trigger fresh insights into the viewers’ eye to being more finely attuned to picking up on cues of OEB beyond what the researcher/animator had deliberately drawn and constructed.